No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
User's Action and Decision Making of Retweet Messages towards Reducing Misinformation Spread during Disaster
Abstract
The online social media such as Facebook, Twitter and YouTube has been used extensively during disaster and emergency situation. Despite the advantages offered by these services on supplying information in vague situation by citizen, we raised the issue of spreading misinformation on Twitter by using retweets. Accordingly, in this study, we conduct a user survey (n=133) to investigate what is the user's action towards spread message in Twitter, and why user decide to perform retweet on the spread message. As the result of the factor analyses, we extracted 3 factors on user's action towards spread message which are: 1) Desire to spread the retweet messages as it is considered important, 2) Mark the retweet messages as favorite using Twitter “Favorite” function, and 3) Search for further information about the content of the retweet messages. Then, we further analyze why user decides to perform retweet. The results reveal that user has desire to spread the message which they think is important and the reason why they retweet it is because of the need to retweet, interesting tweet content and the tweet user. The results presented in this paper provide an understanding on user behavior of information diffusion, with the aim to reduce the spread of misinformation using Twitter during emergency situation.
... The following behavioral beliefs that could entice users to share online rumor denials were identified: Sharing denials helps to spread the truth [5,15], and reduce individuals' belief in rumors [4,5]. It decreases individuals' likelihood to engage in rumor sharing behavior [5,16]. Sharing denials reduces individuals' chances of being deceived by rumors [17,18]. ...
... Sharing denials reduces individuals' chances of being deceived by rumors [17,18]. It allows users to engage in conversation through which individuals seek other related information about the situation to meet their informational needs [16,19,20]. This collaborative information seeking behavior and social exchange is notable in drawing attention of others in the online community [5,19]. ...
... The tendency of sharing denials could be high among users who are motivated to help others and feel a sense of belongingness to the online community [20,21]. Individuals' high perceived importance of messages is also responsible in the formation of behavioral intention of sharing those messages [16]. Individuals tend to share denials if they are confident of their knowledge and ability to assess the veracity of the messages [20,21]. ...
In the era of social media, rumors spread faster and wider than ever before. After a rumor spreads, its effect can be curbed by issuing online refutation messages known as denials. Notwithstanding the potential of denials to reduce Internet users' likelihood to be misinformed, they generally remain less pervasive than rumors. Hence, there is a need to identify how users can be enticed to share denials. Informed by the literature, this paper argues that users' salient beliefs about sharing rumor denials could influence their intention to share such messages. Salient beliefs refer to beliefs about a behavior that are cognitively easy to access at any moment, and serve as primary determinants of performing the behavior. As a part of a larger ongoing project, this paper conducts a survey to identify salient beliefs about sharing rumor denials. The following salient beliefs were identified: Sharing denials help to spread the truth. Friends and the online community would encourage the behavior of sharing rumor denials. Source credibility of denials facilitates sharing of such messages. Significance of the findings and future research directions are highlighted.
... Due to the high level of uncertainty and the information need of the people involved, crisis situations are vulnerable to the spread of rumors (Oh et al., 2013). Although rumoring itself is a necessary process, the spread of false information can lead to panic reactions and false accusations and it might even constrain the work of emergency agencies (Abdullah et al., 2015). Thus, the dissemination of false rumors can be a security threat. ...
... The development of the rumor and debunking messages over time underlines that the reach of rumors in crisis situations depends greatly on temporal factors. This situation is very different from political rumors, which are more persistent (Abdullah et al., 2015). Although that might lead to the conclusion that the influence of rumor messages is limited, it needs to be stressed how dangerous the spread of false information in crisis situations can be. ...
As false information may spread rapidly on social media, a profound understanding of how it can be debunked is required. This study offers empirical insights into the development of rumors after they are debunked, the various user groups who are involved in the process, and their network structures. As crisis situations are highly sensitive to the spread of rumors, Twitter posts from during the 2017 G20 summit are examined. Tweets regarding five rumors that were debunked during this event were manually coded into the following categories: rumor, debunking message, uncertainty about rumor, uncertainty about debunking message, and others. Our findings show that rumors which are debunked early and vehemently by official sources are the most likely to be stopped. When individuals participate in the process, they typically do so by sharing uncommented media content, as opposed to contributing user-generated content. Depending on the conditions in which a rumor arises, different network structures can be found. Since some rumors are easier for individuals to verify than others, our results have implications for the priorities of journalists and official sources.
... (p.6) Subsequent studies have indicated similar factors, in addition to general factors such as information sharing, self-expression, and social interaction or social capital building (Abdullah et al. 2017;Lee et al. 2012;Park and Jeong 2011;Recuero et al. 2011). For instance, Abdullah et al. (2015) found that people retweeted a post because they believe the post is important, an indication being that it is from official account or trusted sources. Lee et al. (2014) also found that people tended to retweet a post that contained a link to a significant report from a reputable media news source, as they believe such a post is more trustworthy. ...
... Factors affecting retweeting (when requested): -Trustworthiness of the content to be spread (e.g., because it contained a link to a significant report from a reputable media news source) -Content relevance (e.g., because it happened in the retweeter's neighborhood) -Message contained valuable information and was helpful to society (e.g., the retweeter think the information is valuable) Abdullah et al. (2015) Disaster information Survey -Need to retweet (people believe it is important to spread the information, that the tweet is related to one's situation, and is from official account or trusted sources) -Interesting tweet content -Tweet user (e.g., which followers have retweeted) Boehmer and Tandor (2015) Sport news Survey -User characteristics: level of interest in a tweet topic, perceived relevance of the tweet, how similarity of the tweet information with personal opinion, and perception of how a tweet would affect followers -Content-related characteristics: tweet's style, informativeness, and originality -Source characteristics: perceived source credibility and likeability Retweeting behavior is an outcome of the influence from the post (e.g., a post with rich information) and the influential users. ...
Twitter is an emerging form of news media with a wide spectrum of participants involving in news dissemination. Owing to their open and interactive nature, individuals, non-media, and non-commercial participants may play a greater role on this platform; thus, it is deemed to disrupt conventional media structures and introduce new ways of information flow. While this may be true in certain aspects in news dissemination such as allowing a broader range of participants, the authors' analysis of the involvement and influence of the different participant types, based on a large tweets dataset collected during the Ukraine's conflict event (2013-2014), portrays a different picture. Specifically, the results unveil that while non-commercial participants were the most “involved” in generating tweets about the news event, the retweets they attracted, a common measure of influence, were among the lowest. In contrast, mass media and sources related to journalists, professional associations and commercial organizations garnered the highest retweets.
... Retweets induces higher social presence (Lim & Lee-Won, 2017). Literature points out users on Twitter retweet those tweets which they find interesting and important (Abdullah, Nishioka, Tanaka, & Murayama, 2015). Tweets expressing certainty are retweeted more as compared to the others ( Runge et al., 2013). ...
... On the basis of our findings, it can be concluded that social influencer CEOs are inducing higher social presence on Twitter as compare to the fortune CEOs (Lim & Lee-Won, 2017). Literature indicates users on Twitter retweet those tweets which they find interesting and important for the society ( Abdullah et al., 2015). Thus it can be inferred that social presence on Twitter is highlighting that tweets of social influencer CEOs are more relevant; or possibly social influencer CEOs are strategically framing their tweets for attracting this social capital. ...
Social media had been extensively used for communication and networking purposes among corporates. Literature indicates social media platforms had also been used by the firms for building relationship with different stakeholders (i.e. customers, employees, investors and neighbouring communities). But there exists a gap in literature, whether social capital present on social media can be used for building corporate reputation (CR) in the society. Literature indicates corporate social responsibility (CSR) impacts CR. Therefore this study explores how CSR messages on social media impacts CR. For this study assumption was made that all the concerns and issues raised by Sustainable Development Goals (SDGs) are the social responsibilities of the CEOs. The study tries to explore this gap by analysing the tweets posted by two group of CEOs, i.e. CEOs in top 200 fortune companies and top 100 social influencer CEOs. Top 100 social influencer CEOs on social media were identified from the Hootsuite.com. Social influencer CEOs are having tremendous amount of influence on Twitter. The statistically test performed on the two group of the CEOs depicts there is significant difference in the number of CSR messages posted by fortune CEOs and social influencer CEOs. Results reveals social influencer CEOs had posted 5.97 times more CSR messages on Twitter as compared to fortune CEOs, which in turn may had led to better CR, in terms of shares and likes by social capital present on Twitter. The study reveals may be social influencer CEOs through CSR messages are trying to engage stakeholders strategically on Twitter. This is an open question at present, therefore future researchers can explore this in more details. The managerial implication of the study for CEOs and firms had been highlighted in the study.
... Misinformation and uncertainty can influence the policy process through a variety of channels. Social media has facilitated the rapid dissemination of misinformation (Abdullah et al. 2015;Bessi et al. 2015;Berinsky 2015;Jin 2014), which organized interests have intentionally propagated to influence policy outcomes (Bedford 2010). Misinformation can also be spread through jurisprudence. ...
... Sharing embodied experiential knowledge can further challenge and destabilize the credibility of more orthodox sources of professional expertise and foster distrust of it (Kata 2010;Bülow 2004). This process is expedited through the internet and social media, where rumors and misinformation related to health care topics are rapidly disseminated (Berinsky 2017;Abdullah et al. 2015;Bessi et al. 2015;Jin et al. 2014;Kata 2010). For instance, online social networks continue to circulate misinformation about the disproven link between the measles, mumps, and rubella (MMR) vaccine and autism, which has largely fueled parents' decisions to forego vaccinations for their children (Chatterjee 2008;Wolfe et al. 2002;Nasir 2000). ...
Since 2010, many of the policies emerging in the states are designed around the idea of “abortion regret;” a scientifically discredited assertion that abortion causes long-term health problems for women. Studies have examined the legal significance of regret claims in case law, and the role scientific misinformation and uncertainty have in the policy process. However, scholars have given less attention to the intersection between abortion regret experiences and misinformation. We address this gap in the literature by examining how antiabortion activists' experiential knowledge continues to reinforce and legitimize misinformation contained in state policies. We explore the process of substantiating abortion regret misinformation by its attachment to activists' experiential expertise. Based on twenty-three interviews with antiabortion activists, we argue that misinformation receives validation through the certainty of experiential knowledge, which activists mobilize around and use as a source of evidence in the policy process. This article is protected by copyright. All rights reserved.
... As relevant research has such limitations, we refer to rumor theory as our theoretical foundation for the following reasons. First, although rumor theory was developed to explain the spread of rumors in war (Allport & Postman, 1946, 1947, studies suggest that it can explain message retransmission in general (Abdullah et al., 2015), not just rumors or fake news (Cheng et al., 2024;Shirish & Kotwal, 2023). Second, rumor theory has been widely used to study how information spreads in disasters (e.g., Larsen, 1954;Ma, 2008), as disasters often create information vacuums and may induce rumors and gossip (Pang, 2013). ...
Retransmitted messages online can have profound effects on disaster response; however, existing literature provides an incomplete account of why messages are retransmitted on social media in disasters. In particular, there is a need to theorize the capabilities of the communication tools used for sending messages, because nowadays people can send messages online via different tools. This paper aims to theorize and explain how the capabilities of communication tools affect message retransmission by affecting the generation of message characteristics. To test our account, we collected and coded Twitter data from three disasters, and employed five logistic regressions to test our hypotheses. Our results confirm our expectations that compared to messages sent from desktops, messages sent from mobile devices are less likely to be helpful and verifiable, but are more likely to have visual attachments and expressions of anxiety.
... developed by Howat 2015, 2017) was implemented, which is based on a combination of the vertical line locus method (Schenk 1999) and an adjustment of the rational polynomial coefficients that describe the satellite orbit to produce surface models. The SETSM algorithm has been applied previously for development of the ArcticDEM, enabling glacier change detection studies from topographic change models (Abdullah et al. 2015;Dai et al. 2018). Recent applications of the SETSM algorithm in geoscience include geomorphology (Atwood andWest 2022, Corsa et al. 2022), volcanology (Dai et al. 2022), and glaciology (King et al. 2020, Melling et al. 2024. ...
Knowledge of landslide volumes is needed to connect landslide trigger, geometry, and mechanism with the mechanical characteristics of the displaced soil and rock masses. While landslide volume inventories of widespread events are scarce, increasing availability of high-resolution imagery time-series presents new opportunities for developing volume inventories in terms of scale and resolution. Here we present a novel 3D landslide volume dataset using topographic differencing methods to evaluate the potential for such studies in future hazard and geomorphic research. Remotely sensed stereo optical imagery collected shortly after the 2015 Lefkada Mw6.5 earthquake event in western Greece was used to create two post-event DSM surfaces using Worldview-3 satellite images with the SETSM algorithm and UAV-based optical imagery using Structure from Motion (SfM). We demonstrate good agreement between methods for mapping of ~ 700 landslides. Elevation change more accurately identifies source areas on steep slopes compared to imagery alone, distinguishes deeper landsliding from shallow ravel, and reveals complex patterns that are not well approximated by simple landslide slip surface geometries. Statistical relationships are sensitive to aspects of the methodology, namely topographic resolution, accurate image registration, and estimation of 2D (plane) area versus 3D surface area. These analyses also raise the question of what constitutes a single landslide mass in these events and thus the utility of landslide frequency as a statistical measure. As we achieve resolution that surpasses ground-based field studies and removes the selection bias of focusing only on select well-defined, deep landslides, new patterns of ground failure emerge to which mapping and statistical data interpretations will need to adapt.
... This may mean that Twitter users consider tweets about 'Extreme weather and climate change' to be interesting, relevant and thus worth sharing. This insight is consistent with Abdullah et al. (2015) and Lee and Yu (2020), who show that content relevance impacts retweetability. Furthermore, from manual inspection of the content of the tweets in each topic, we found that numbers and prepositions were used more frequently in tweets that shared information about extreme weather. ...
Increasing average temperatures and heat waves are having devasting impacts on human health and well-being but studies of heat impacts and how people adapt are rare and often confined to specific locations. In this study, we explore how analysis of conversations on social media can be used to understand how people feel about heat waves and how they respond. We collected global Twitter data over four months (from January to April 2022) using predefined hashtags about heat waves. Topic modelling identified five topics. The largest (one-third of all tweets) was related to sports events. The remaining two-thirds could be allocated to four topics connected to communication about climate-related heat or heat waves. Two of these were on the impacts of heat and heat waves (health impacts 20%; social impacts 16%), one was on extreme weather and climate change attribution (17%) and the last one was on perceptions and warning (13%). The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia, the USA and South Asia (India and Pakistan), indicating that people posting tweets were aware of the threat from heat and its impacts on the society. Among the words frequently used within the topic ‘Social impacts’ were ‘air-conditioning’ and ‘electricity’, suggesting links between coping strategies and financial pressure. Apart from analysing the content of tweets, new insights were also obtained from analysing how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were then shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence that misinformation about climate change-related heat is widespread.
... Major reasons why people rely upon social media platforms during a disaster are that these are convenient to use, have the ability to 'mass sending' and are known to be time and cost-effective (Peary, Shaw, and Takeuchi;White, Plotnick, Kushma, Hiltz, and Turoff in Abdullah et al., 2015, p. 32). On the other hand, it is already known that current social media platforms allow misinformation to spread quickly -a fact that, in crisis situations, could widely jeopardize public safety, public security, and emergency rescue attempts (Johnson, 2020;Abdullah et al., 2015). Misinformation, i.e., inaccurate or misleading information with or without the intention to cause harm, diffuses faster on social media than the truth (Vosoughi, Roy, & Aral, 2018). ...
Social media platforms like Twitter are extensively used during crises which seems to be a double-edged sword. On one hand, these platforms have an invaluable potential to become a reliable source of information and communication-tool for those involved in crisis situations. On the other hand, it is already known that current social media platforms allow misinformation to spread quickly - a fact that, in crisis situations, could widely jeopardize public safety. The reasons why misinformation spreads so quickly on Twitter in crisis situations can be traced back to several factors such as the platform’s design choices, as well as the user's lack of critical engagement with the information, but, as we argue in this article, also because of an inherent value hierarchy embedded in the design of the user interface. In this paper, we aim to first clarify what can be done effectively by Twitter to mitigate the spread of misinformation in crisis situations. By using a Value Sensitive Design framework, we argue that the current Twitter design promotes a multiplicity of values that are implemented in the user-interface design in ways that lead to conflicting effects on misinformation. We thus argue that a design intervention needs to change both the current value hierarchy and the design implementations of the values at stake. Our paper concludes with several design recommendations for elevating the user's critical engagement as well as a discussion about the moral implications of trying to streamline Twitter’s user interface design towards favouring truth-telling in situations of crisis.
... The results suggest that Twitter users consider tweets on the 'Extreme weather conditions and climate change' topic to be interesting, relevant and thus worth sharing. This insight is consistent withAbdullah (2015) andLee et al. (2014), who revealed that content relevance ...
Increasing average temperatures and heat waves have devasting impacts on human health and well-being but studies on heat impacts and how people adapt are rare and often confined to specific locations. In this study we aimed to explore how social media data can be used to generate knowledge about how people feel about heat waves through an analysis of their conversations. We collected global Twitter data over two months (January and February 2022) using predefined hashtags about heatwaves. Topic modelling revealed five clusters of which three were related to communications about climate change related heat, covering 76% of all tweets. These three clusters included one on the impacts of heat and heatwaves (34%), one on perceptions of weather and heat (28%) and one on extreme weather and climate change (14%). The remaining 24% of tweets were on sport events or films and music. The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia and India, indicating that people are aware of the threat from heat and its impacts on the society, although not much could be learned in terms of personal coping and preparedness strategies. Apart from the content of tweets, a great deal could be learned in terms of how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence of wide-spread misinformation about climate change related heat.
... Several studies have contributed to this field by proposing methods for predicting the results of important events, such as games, and political elections, using data on the volume of retweet ( Abdullah, Nishioka, Tanaka, & Murayama, 2015 ;Liang et al., 2016 ). Some studies explored the reasons why users retweet certain information without applying machine learning techniques for prediction. ...
In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic.
... The fake news problem is not a sudden syndrome in the midst of the COVID-19 pandemic. There have been many studies on media reliability problems due to misinformation in various national disaster situations such as the Victoria bushfire, Haiti Earthquake, The Great East Japan Earthquake, and Hurricane Sandy (Abdullah et al., 2015). Given that fake news stimulates readers to disseminate the contents actively (Nielsen and Graves, 2017), studies on how to deal with fake news have been vigorously conducted. ...
As the battle with COVID-19 continues, an Infodemic problem has been raised. Even though the distribution of false news in national disaster situations has been reported for a long time, little attention has been given to the quantitative research of the fake news problem from the audience’s perspective. This study, therefore, aims to estimate how much tax taxpayers would gladly pay for a virtual public-run fact-checking system. Using a one-and-one-half bounded dichotomous choice contingent valuation method, a survey was conducted on 525 respondents in Korea, and the spike model was applied to distinguish zero willingness-to-pay (WTP). The results show that a household’s WTP for the public fact-checking system is 10,652 KRW (9 USD), on average, in the form of income tax for five years. Given the amount is a regular payment in perpetuity, the total WTP is estimated at 23 billion KRW ($196M) every year. The result also shows that an individual’s WTP increases as his or her psychological damage is caused by fake news is high, as well as his or her high reliance on news in a disaster situation.
... Content importance was also proved to be a critical factor that drove Twitter users to share disaster information during crises. During disasters, individuals are also more likely to repost when they recognize the importance of messages [66]. Thus, this study posits that content importance can promote the sharing of COVID-19 fact-checks. ...
Widespread misinformation about COVID-19 poses a significant threat to citizens long-term health and the combating of the disease. To fight the spread of misinformation, Chinese governments have used official social media accounts to participate in fact-checking activities. This study aims to investigate why citizens share fact-checks about COVID-19 and how to promote this activity. Based on the elaboration likelihood model, we explore the effects of peripheral cues (social media capital, social media strategy, media richness, and source credibility) and central cues (content theme and content importance) on the number of shares of fact-checks posted by official Chinese Government social media accounts. In total, 820 COVID-19 fact-checks from 413 Chinese Government Sina Weibo accounts were obtained and evaluated. Results show that both peripheral and central cues play important roles in the sharing of fact-checks. For peripheral cues, social media capital and media richness significantly promote the number of shares. Compared with the push strategy, both the pull strategy and networking strategy facilitate greater fact-check sharing. Fact-checks posted by Central Government social media accounts receive more shares than local government accounts. For central cues, content importance positively predicts the number of shares. In comparison to fact-checks about the latest COVID-19 news, government actions received fewer shares, while social conditions received more shares.
... The topics or emotions which peak during a crisis are more important to the users. Abdullah et al. (2015) performed a study to show that, during a crisis, users retweet what they think is important; this is different from the user thinking it is correct. The same applies to user behavior during a pandemic-users would more often retweet what they think is important than what is correct. ...
COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.
... A very different context in which the effective use of information systems is increasingly important is the use of social media in disasters, such as the use of Twitter to communicate during and about floods, wildfires, and terrorism. In such contexts, social media can cause negative impacts if used ineffectively (e.g., arousing panic) (Abdullah et al. 2015), but it can equally bring about positive impacts if used effectively (e.g., minimizing impacts and supporting recovery) (Chan 2014). To understand effective use in this context, we followed a dual-strategy of making deductions from RT while also analyzing social media (in particular, Twitter) data from three disasters (Oklahoma tornado, 2013;Boston Marathon Bombing, 2013;and New South Wales Bushfires, 2013). ...
... Misinformation spread has also been modeled, in the context of social networks (Kempe et al., 2003). More recent research uses sophisticated techniques to model the concurrent spread of both misinformation and correct information (Nguyen et al., 2012;Tambuscio et al., 2015;Abdullah et al., 2015), for both homogenous and heterogenous populations. ...
Consensus is viewed as a proxy for truth in many discussions of science. When a consensus is formed by the independent and free deliberations of many, it is indeed a strong indicator of truth. Yet not all consensuses are independent and freely formed. We investigate the role of dependence and pressure in the formation of consensus, showing that strong polarization , external pressure, and dependence among individuals can force consensus around an issue, regardless of the underlying truth of the affirmed position. Dependence breaks consensus , often rendering it meaningless; a consensus can only be trusted to the extent that individuals are free to disagree with it.
... Last but not least, misinformation is often found in social media stream and has been seen in the context of disasters [41]. Such misinformation can mislead disaster response efforts and waste limited resources by spreading rumors or creating panic situations. ...
Flooding management requires collecting real-time onsite information widely and rapidly. As an emerging data source, social media demonstrates an advantage of providing in-time, rich data in the format of texts and photos and can be used to improve flooding situation awareness. The present study shows that social media data, with additional information processed by Artificial Intelligence (AI) techniques, can be effectively used to track flooding phase transition and locate emergency incidents. To track phase transition, we train a computer vision model that can classify images embedded in social media data into four categories – preparedness, impact, response, and recovery – that can reflect the phases of disaster event development. To locate emergency incidents, we use a deep learning based natural language processing (NLP) model to recognize locations from textual content of tweets. The geographic coordinates of the recognized locations are assigned by searching through a dedicated local gazetteer rapidly compiled for the disaster affected region based on the GeoNames gazetteer and the US Census data. By combining image and text analysis, we filter the tweets that contain images of the “Impact” category and high-resolution locations to gain the most valuable situation information. We carry out a manual examination step to complement the automatic data processing and find that it can further strengthen the AI-processed results to support comprehensive situation awareness and to establish a passive hotline to inform rescue and search activities. The developed framework is applied to the flood of Hurricane Harvey in the Houston area.
... Continuously monitoring over the tweets is necessary for collection of data. There are some of the examples that we used in our database that are for e.g.:-my life comes to an end, struggling through depression for about a year now and my mom died suffering from a stage four brain cancer and it's been hard [10]. These data are manually annotated and for every tweet t1 it indicates the suicidal ideation and belongs to data-set. ...
With a growth in the use of the social media, we have witnessed a positive connection between the demonstration of Suicidal ideation on social networking sites (such as Twitter) and the suicidal cases. One of the most admired and extensively used online social network sites is Twitter. Twitter becomes the mean where every individual can share their emotions whether the emotions are positive negative or neutral. The aim of this study is to design a model for individuals that run the higher risk of committing suicide. By studying different predictors of suicide such as depression, anxiety, hopelessness, hypersomnia, Insomnia and stress the model will be created. Techniques such as that of text mining will be utilized for the prediction purposes. The research uses text mining process for the analysis of data, and on the basis of this analysis, a model is developed that will help predicting suicidal behaviours present in the individual. Then, the Machine Learning algorithms such as Decision Tree (DT) and Naïve Bayes (NB) are used for classification. Which are then compared for the prediction purposes. Data used in such models requires unceasing monitoring; in addition to monitoring, to elevate the prediction’s accuracy, data needs to be updated periodically too.
... Last but not least, misinformation is often found in social media stream and has been seen in the context of disasters [41]. Such misinformation can mislead disaster response efforts and waste limited resources by spreading rumors or creating panic situations. ...
Flooding management requires collecting real-time onsite information widely and rapidly. As an emerging data source, social media demonstrates an advantage of providing in-time, rich data in the format of texts and photos and can be used to improve flooding situation awareness. The present study shows that social media data, with additional information processed by Artificial Intelligence (AI) techniques, can be effectively used to track flooding phase transition and locate emergency incidents. To track phase transition, we train a computer vision model that can classify images embedded in social media data into four categories -- preparedness, impact, response, and recovery -- that can reflect the phases of disaster event development. To locate emergency incidents, we use a deep learning based natural language processing (NLP) model to recognize locations from textual content of tweets. The geographic coordinates of the recognized locations are assigned by searching through a dedicated local gazetteer rapidly compiled for the disaster affected region based on the GeoNames gazetteer and the US Census data. By combining image and text analysis, we filter the tweets that contain images of the ``Impact'' category and high-resolution locations to gain the most valuable situation information. We carry out a manual examination step to complement the automatic data processing and find that it can further strengthen the AI-processed results to support comprehensive situation awareness and to establish a passive hotline to inform rescue and search activities. The developed framework is applied to the flood of Hurricane Harvey in the Houston area.
... Continuously monitoring over the tweets is necessary for collection of data. There are some of the examples that we used in our database that are for e.g.:-my life comes to an end, struggling through depression for about a year now and my mom died suffering from a stage four brain cancer and it's been hard [10]. These data are manually annotated and for every tweet t1 it indicates the suicidal ideation and belongs to data-set. ...
... Most notably, the majority of works have investigated fraudulent accounts and credibility of news in online social networks such as twitter [21]- [26]. A few other works have proposed models for disseminating good information in social networks to mitigate the effect of misinformation, thereby improving reliability [27]- [31]. Misinformation and manipulation in financial limit order markets are probably studied far more in-depth, especially using game-theoretic approaches (see e.g. ...
This work is a technical approach to modeling false information nature, design, belief impact and containment in multi-agent networks. We present a Bayesian mathematical model for source information and viewer's belief, and how the former impacts the latter in a media (network) of broadcasters and viewers. Given the proposed model, we study how a particular information (true or false) can be optimally designed into a report, so that on average it conveys the most amount of the original intended information to the viewers of the network. Consequently, the model allows us to study susceptibility of a particular group of viewers to false information, as a function of statistical metrics of the their prior beliefs (e.g. bias, hesitation, open-mindedness, credibility assessment etc.). In addition, based on the same model we can study false information "containment" strategies imposed by network administrators. Specifically, we study a credibility assessment strategy, where every disseminated report must be within a certain distance of the truth. We study the trade-off between false and true information-belief convergence using this scheme which leads to ways for optimally deciding how truth sensitive an information dissemination network should operate.
... The most common method for information diffusion in social network platforms is retweeting, which allows user to repost others' tweets in their own content stream. Although a number of features that might affect retweetability of tweets, such as user profile, content freshness and temporal informaion, have been analyzed by [6], the user survey results recently have been indicated that user's personal preferences and social influence from target user are the two most important factors when users consider whether or not to retweet a given message [1,4]. Therefore, we will quantify two strength metrics: interest similarity between user and message, and social influence between two users. ...
Social behaviors such as retweetings, comments or likes are valuable information for human activities analysis. We focus here on user's retweeting behavior which has been considered as a key mechanism of information diffusion in social networks. Since we can only observe on which messages user retweet. It is a typically one-class setting which only positive examples or implicit feedback can be observed. However, few research works on retweeting prediction consider one-class setting. In this paper, we analyze and study the fundamental factors that might affect retweetability of a tweet, and then employ one-class collaborative filtering method by quantitatively measure the user personal preference and social influence between users and messages to predict user's retweeting behavior. Experimental results on a real-world dataset from social network show that the proposed method is effective and can improve the performance of the one-class collaborative filtering over baseline methods through leveraging weighted negative examples information.
The perceived credibility of misinformation significantly enhances the public’s willingness to re-disseminate the misinformation, leading to severe social and economic consequences. Although previous studies have extensively analyzed how the source of the misinformation and its recipients influence the public’s perceived credibility, which aspect has a greater impact remains to be explored. This study focuses on analysing 982 instances of misinformative microblogs, and uses Bayesian Networks to establish an evaluation system for assessing the public’s perceived credibility of their misinformation on the basis of the sources and recipients. The goal of this study is to explore the main aspects and factors that affect the credibility of the public perception of false information. Our findings show that recipients have a more significant influence on perceived credibility than sources. Additionally, the three most important indicators influencing the public’s perceived credibility of misinformation are the activities of the misinformation recipients, the attention duration given by recipients to the misinformation, and the format of the misinformation source. By identifying key factors influencing the public’s perceived credibility of online misinformation, this study can provide targeted guidance for reducing the public’s trust inmisinformation.
With the prevalence and increasing impact of misinformation on social media, numerous studies have explored the characteristics of misinformation. However, we lack a holistic understanding of what factors contribute to the viral spread of misinformation on social media and what strategies have been proposed to combat the phenomenon. Given the importance of these questions to misinformation research, this study aims to (a) provide a systematic and structured overview of the factors that influence the spread of misinformation by analyzing the four vital elements of information communication, namely, source, message, context, and receiver and (b) summarize the current state of research on strategies against the spread of misinformation on social media from various perspectives and discuss their advantages, disadvantages, and effectiveness. Following the process of a systematic literature review, this study identifies and analyzes 423 relevant articles. Finally, we highlight research gaps in the existing literature and recommend directions for future research.
Misinformation spreads fast in times of crises, corroding public trust and
causing further harm to already vulnerable communities. In earthquake
seismology, the most common misinformation and misleading popular
beliefs generally relate to earthquake prediction, earthquake genesis,
and potential causal relations between climate, weather and earthquake
occurrence. As a public earthquake information and dissemination center, the
Euro-Mediterranean Seismological Center (EMSC) has been confronted many
times with this issue over the years. In this paper we describe several types of
earthquake misinformation that the EMSC had to deal with during the 2018
Mayotte earthquake crisis and the 2021 La Palma seismic swarm. We present
frequent misinformation topics such as earthquake predictions seen on our
communication channels. Finally, we expose how, based on desk studies
and users’ surveys, the EMSC has progressively improved its communication
strategy and tools to fight earthquake misinformation and restore trust in
science. In this paper we elaborate on the observed temporality patterns for
earthquake misinformation and the implications this may have to limit the
magnitude of the phenomenon. We also discuss the importance of social,
psychological and cultural factors in the appearance and therefore in the fight
against misinformation. Finally, we emphasize the need to constantly adapt to
new platforms, new beliefs, and advances in science to stay relevant and not
allow misinformation to take hold
This study examined the relationships between expressions in Tweets, topic choices, and subjective well-being among undergraduates in Japan. The authors conducted a survey with 304 college students and analyzed their Twitter posts using natural language processing (NLP). Based on those who posted over 50 tweets, the authors found that (1) users with higher levels of social skills had fewer negative tweets and higher levels of subjective well-being; (2) frequent users posted both positive and negative tweets but posted more negative than positive tweets; (3) users with fewer negative tweets or with more positive tweets had higher levels of subjective well-being; and (4) “safe” topics such as social events and personal interests had a positive correlation with the users' subjective well-being, while debatable topics such as politics and social issues had a negative correlation with the users' subjective well-being. The findings of this study provide the foundation for applying NLP to analyze the social media posts for businesses and services to understand their consumers' sentiments.
Purpose
This study investigated the relationship between generalised trust and psychological well-being in college students, considering the social support obtained from their social networks via Twitter and face-to-face (FTF) interactions. Initially, the authors planned to collect data at the beginning of the first semester in 2019 for fine-tuning the model as a pilot study, and in 2020 for the main study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, the data helped authors to analyse changes in young people's psychological situation before and during the pandemic in Japan.
Design/methodology/approach
The study conducted a self-report survey targeting college students in the Kanto region in Japan. Data were collected from mid-May to the end of June 2019, as well as in early to mid-June 2020, with 304 and 584 responses, respectively. The collected data were analysed using structural equation modelling and a multiple regression analysis.
Findings
The findings using the 2019 data set indicated that (a) students mostly used Twitter for information gathering and sharing of hobbies, and they received both informatics and emotional support from Twitter, and from FTF interactions; (b) there were direct positive effects of generalised trust and social skills on their psychological well-being; and (c) students with lower levels of generalised trust tended to interact with very intimate individuals using Twitter to obtain social support, which did not have any effects on their improvement of psychological well-being. From the 2020 data set, the authors also found that, like 2019, generalised trust and social skills had direct effects on the improvement of psychological well-being. Additionally, we observed that students spent more time using Twitter and received more emotional support from it, as most people tried not to meet other people in person due to the first State of Emergency in Japan. Similarly, the authors found that in 2019, only social support from very intimate partners via FTF communication had slightly significant effects on improving their psychological well-being, whereas in 2020, their expectation for social networks via FTF had decreased their levels of psychological well-being, but their social support from Twitter had slightly significant effects on their improvement of psychological well-being. One of the main reasons for this might be due to the challenge of meeting with others in person, and therefore, social support from Twitter partially played a role that traditionally was only beneficial through FTF communication.
Originality/value
We understand that this is one of the few social psychological studies on social media that collected data both before and during the COVID-19 pandemic. It provides unique evidence in demonstrating how the COVID-19 pandemic has changed college students communication behaviours.
Background
Micro‐blogging services empower health institutions to quickly disseminate health information to many users. By analysing user data, infodemiology (i.e. improving public health using user contributed health related content) can be measured in terms of information diffusion.
Objectives
Tweets by the WHO were examined in order to identify tweet attributes that lead to a high information diffusion rate using Twitter data collected between November 2019 and January 2020.
Methods
One thousand hundred and seventy‐seven tweets were collected using Python's Tweepy library. Afterwards, k‐means clustering and manual coding were used to classify tweets by theme, sentiment, length and count of emojis, pictures, videos and links. Resulting groups with different characteristics were analysed for significant differences using Mann–Whitney U‐ and Kruskal–Wallis H‐tests.
Results
The topic of the tweet, the included links, emojis and (one) picture as well as the tweet length significantly affected the tweets’ diffusion, whereas sentiment and videos did not show any significant influence on the diffusion of tweets.
Discussion
The findings of this study give insights on why specific health topics might generate less attention and do not showcase sufficient information diffusion.
Conclusion
The subject and appearance of a tweet influence its diffusion, making the design equally essential to the preparation of its content.
Over the last few years, social media has expanded to become a key platform for news dissemination and circulation, and a key orginator and propogator of 'fake news'.. Nations, governments, organisations and societies are now coming to terms with the unpredictable and debilitating consequences of fake news.
The propagation of news containing falsehoods has been linked to an increase in measles cases, surges in youth crimes, the spread of pseudo-science, compromised national security, and more. Some even perceive it as a global threat to democratic systems around the world. In this book, the authors examine factors influencing the spread of fake news, and suggest ways to combat it by exploring the key elements which enable and facilitate this phenomenon.
Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms.
Retweeting provides an efficient way to expand information diffusion in social networks, and many methods have been proposed to model user’s retweeting behaviors. However, most of existing works focus on devising an effective prediction method based on social network data, and few research studies explore the data characteristic of retweeting behaviors which is typical binary discrete distribution and sparse data. To this end, we propose two novel retweeting prediction models, named Binomial Retweet Matrix Factorization (BRMF) and Context-aware Binomial Retweet Matrix Factorization (CBRMF). The two proposed models assume that retweetings are from binomial distributions instead of normal distributions given the factor vectors of users and messages, and then predicts the unobserved retweetings under matrix factorization. To alleviate data sparsity and reduce noisy information, CBRMF first learns user community by using community detection method and message clustering by using short texts clustering algorithm from social contextual information on the basis of homophily assumption, respectively. Then CBRMF incorporates the impacts of homophily characteristics on users and messages as two regularization terms into BRMF to improve the prediction performance. We evaluate the proposed methods on two real-world social network datasets. The experimental results show BRMF achieves better the prediction accuracy than normal distributions based matrix factorization model, and CBRMF outperforms existing state-of-the-art comparison methods.
The literature currently lacks an understanding of how denials can be crafted to effectively debunk rumors on social media. Underpinned by the theory of planned behavior, this research develops denials by incorporating salient beliefs to enhance users’ likelihood to share such messages. Two related studies were conducted. The first was a survey of 276 participants to identify salient beliefs that could be incorporated to develop rumor denials. The following salient beliefs were identified in the survey: (i) Sharing denials helps to spread the truth; (ii) Friends and the online community encourage the behavior of sharing denials; and (iii) Source credibility of denials encourages sharing. From among the pool of survey participants, 206 took part in a second study that employed an experiment to measure the efficacy of the developed denials. The experiment revealed that denials incorporating all the salient beliefs had the greatest potential to influence users’ likelihood of sharing. With a theory-driven approach to develop denials, this research offers insights to practitioners such as social media managers and website authorities on ways to debunk rumors.
The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.
Background
Twitter offers a platform for rapid diffusion of information and its users' attitudes and behaviors. Insights about information propagation via retweets (the message forwarding function) offer observable explanations of ways in which modern human interactions get organized in the form of online networks, and contextualized in the form of public health, policy decisions, disaster management, and civic participation. This study conceptualized and validated the Why We Retweet Scale to contextualize retweeting behavior.
Objective
Twitter users were identified using clustering algorithms that consider a users’ position in their network and invited for an online survey. Participants (N = 1433) responded to 19 questions about why they retweet. Exploratory factor Analysis (EFA) was conducted on a scale development sample (70% of original sample), which informed the Confirmatory Factor Analysis (CFA) on a scale testing sample (30% of the original sample). Varimax rotation was used to obtain a rotated factor solution, which resulted in interpretable factors. Demographic differences among scale factors were analyzed using one-way ANOVA or independent samples t-tests.
Results
The final model (χ²21 = 28, RMSEA = .03 [90% CI, 0.00–0.06], CFA = .99, TLI = 0.99) represented a parsimonious solution with 4 factors, measured by 2–3 items each, creating a final scale consisting of 9 items. Factor labels and definitions were: (1) Show approval, “Show support to the tweeter”; (2) Argue, “To argue against a tweet that I disagree with”; (3) Gain attention, “Add followers or gain attention”; and (4) Entertain, “Create humor/amusement”. Demographic differences were also reported.
Conclusions
The Why We Retweet Scale offers a useful conceptualization and assessment of motivations for retweeting. In the future, communication strategists might consider the factors associated with information propagation when designing campaign messages to maximize message reach and engagement on Twitter.
In this paper, we study topic-specific retweet count ranking problem in Weibo. Two challenges make this task nontrivial. Firstly, traditional methods cannot derive effective feature for tweets, because in topic-specific setting, tweets usually have too many shared contents to distinguish them. We propose a LSTM-embedded autoencoder to generate tweet features with the insight that any different prefixes of tweet text is a possible distinctive feature. Secondly, it is critical to fully catch the meaning of topic in topic-specific setting, but Weibo can provide little information about topic. We leverage real-time news information from Toutiao to enrich the meaning of topic, as more than 85% topics are headline news. We evaluate the proposed components based on ablation methods, and compare the overall solution with a recently-proposed tensor factorization model. Extensive experiments on real Weibo data show the effectiveness and flexibility of our methods.
Retweet prediction is a fundamental and crucial task in social networking websites as it may influence the process of information diffusion. Existing prediction approaches simply ignore social contextual information or don’t take full advantage of these potential factors, damaging the performance of prediction. Besides, the sparsity of retweet data also severely disturb the performance of these models. In this paper, we propose a novel retweet prediction model based on probabilistic matrix factorization method by integrating the observed retweet data, social influence and message semantic to improve the accuracy of prediction. Finally, we incorporate these social contextual regularization terms into the objective function. Comprehensive experiments on the real-world dataset clearly validate both the effectiveness and efficiency of our model compared with several state-of the-art baselines.
Nowadays, information can spread rapidly over social networks via the relationships and interactions among people. To reveal the underlying intricate mechanism of information propagation, the problem of repost behavior prediction has recently drawn extensive attention. In this paper, we propose a novel method to measure time-sensitive mutual influence based on temporal behavior patterns of users and develop an efficient algorithm to calculate it via a discretization method. To predict repost behavior more accurately, we introduce another two features of user interests and information content, and respectively design the effective measurements of them to capture their predictive power. We further combine time-sensitive mutual influence with the two features into a feature-based method and evaluate the performance of the proposed method on repost prediction. Finally, extensive experiments have been conducted on a real large-scale microblogging dataset. The experimental results demonstrate that our method can achieve better performance compared to several baseline methods.
To better understand user conversations revolving around antibiotics and antimicrobial resistance (AMR) on Twitter, we used an online data collection and analysis toolkit with full firehose access to collect corpuses of tweets with "antibiotic" and "antimicrobial resistance" keyword tracks. The date range included tweets from November 28, 2015, to November 25, 2016, for both datasets. This yearlong date range provides insight into how users have discussed antibiotics and AMR and identifies any spikes in activity during a particular time frame. Overall, we found that discussions about antibiotics and AMR predominantly occur in the United States and the United Kingdom, with roughly equal gender participation. These conversations are influenced by news sources, health professionals, and governmental health organizations. Users will often defer to retweet and recirculate content posted from these official sources and link to external articles instead of posting their own musings on the subjects. Our findings are important benchmarks in understanding the prevalence and reach of potential misinformation about antibiotics and AMR on Twitter.
This chapter presents the issues on disaster communications. The Great East Japan Earthquake on March 11th, 2011 caused severe damage to the northern coast of the main island in Japan. We report our support activities in Iwate prefecture as well as our findings and experiences. We call disaster communications in this chapter. disaster communications. Following the requests from many organizations and groups of people, we started our support for the disaster area with a few of us in the department of Software and Information Science, Iwate Prefectural University ten days after the disaster. Through our support activities we came across an interesting issue concerning collaboration with people from heterogeneous backgrounds. Disagreements and distrust happened quite easily. We found that trust plays an important role in such communications. In our chapter, we introduce disaster communications as an area for research and practice as well as our trials on the recovery phase after the emergency response.
Understanding retweeting mechanism and predicting retweeting behavior is an important and valuable task in user behavior analysis. In this paper, aiming at providing a general method for improving retweeting behavior prediction performance, we propose a probabilistic matrix factorization model (RTPMF) incorporating user social network information and message semantic relationship. The contributions of this paper are three-fold: (1) We convert predicting user retweeting behavior problem to solve a probabilistic matrix factorization problem; (2) Following the intuition that user social network relationship will affect the retweeting behavior, we extensively study how to model social information to improve the prediction performance; and (3) We also incorporate message semantic embedding to constrain the objective function by making a full use of additional the messages’ content-based and structure-based features. The empirical results and analysis demonstrate that our method significantly outperform the state-of-the-art approaches.
Online social networks have been recently increasingly become the dominant platform of information diffusion by user’s retweeting behavior. Thus, understanding and predicting who will be retweeted in a given network is a challenging but important task. Existing studies only investigate individual user and message for retweeting prediction. However, social influence and selection lead to formation of groups. The intrinsic and important factor has been neglected for this problem. In the paper, we propose a unified user and message clustering based approach for retweeting behavior prediction. We first cluster users and messages into different groups based on explicit and implicit factors together. Then we model social clustering information as regularization terms to introduce the retweeting prediction framework in order to reduce sparsity of data and improve accuracy of prediction. Finally, we employ matrix factorization method to predict user’s retweeting behavior. The experimental results on a real-world dataset demonstrate that our proposed method effectively increases accuracy of retweeting behavior prediction compared to state-of-the-art methods.
Retweeting is the most prominent feature in online social networks. It allows users to reshare another user’s tweets for her followers and bring about second information diffusion. Predicting retweeting behaviors is an important and essential task for advertising product launch, hot event detection and analysis of human behavior. However, most of the methods and systems have been developed for modeling the retweeting behaviors, it has not been fully explored for this problem. In this paper, we first cast the problem of retweeting behaviors prediction as a classification task and propose a formally definition. We then systematically summarize and extract a lot of features, namely user status, content, temporal, and social tie information, for predicting users’ retweeting behaviors. We incorporate these features into Support Vector Machine (SVM) model for our prediction problem. Finally, we conduct extensive experiments on a real world dataset collected from Twitter to validate our proposed approach. Our experimental results demonstrate that our proposed model can improve prediction effectiveness by combining the extracted features compared to the baselines that do not.
Twitter, a popular social media, helps users around the world quickly share and receive information. The way in which Twitter frames health issues – especially controversial issues like emergency contraception (EC) – can influence public opinion. The current study analyzed all English-language EC-related tweets from March 2011 (n = 3535). Variables measured user characteristics (e.g. gender), content (e.g. news, humor), Twitter-specific strategy (e.g. retweet), and certain time periods (e.g. weekends). The analysis applied chi-square and regression analyses to the variables. Tweets most frequently focused on content related to news (27.27%), accessing EC (27.27%), and humor (25.63%). Among tweets that were shared, however, the most common content included humor, followed by personal/vicarious experience. Although only 5.54% of shared tweets mentioned promiscuity, this content category had the strongest odds for being shared (OR = 1.51; p = 0.031). The tweet content with lowest odds of being shared were side effects (OR = 0.24; p < 0.001), drug safety (OR = 0.44; p < 0.001), and news (OR = 0.44; p < 0.001). Tweets with the greatest odds of having been sent on a weekend sought advice (OR = 1.94; p = 0.012), addressed personal or vicarious experience (OR = 1.91; p < 0.001), or contained humor (OR = 1.56; p < 0.001). Similar patterns occurred in tweets sent around St. Patrick’s Day. Only a few differences were found in the ways in which male and female individuals discussed EC on Twitter. In particular, when compared to males, females mentioned birth control (p = 0.002), EC side effects (p = 0.024), and issues related to responsibility (p = 0.003) more often than expected. Study findings offer timely and practical suggestions for public health professionals wanting to communicate about EC via Twitter.
During the 2011 East Japan Earthquake and Tsunami, newly popular social media such as Twitter and Facebook served as a lifeline for directly affected individuals, a means of information sharing, and a way for people inside and outside Japan to volunteer and to provide information-based support to affected individuals. Social media was used to perform vital relief functions such as safety identification, displaced-persons locating, damage information provision, support for disabled individuals, volunteer organization, fund-raising, and moral support systems. This study discusses the potential for public, civil society, and government organizations to utilize social media in disaster preparedness and response.
In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter.
Twitter is an example of social media, which allows its users to post text messages, known as "tweets," of up to 140 characters. A tweet can include a shortened URL that provides further information that cannot be included in the tweet. Does including URLs in tweets influence the forwarding of the tweets during disasters, in which social media is flooded with unverified information? We conducted an experiment to answer this question. The results showed that posting URLs in disaster-related tweets increased rumor-spreading behavior even though the URLs lacked the hyperlink function. We identified some psychological factors that could explain this effect. We conclude by discussing the vulnerability of social media to rumor transmission in light of our results.
Social media have increasingly been used for information exchange during extreme events (EEs). Yet, until recently it had not been systematically studied how government has used and can use social media under circumstances of extreme duress. This research describes how government actually engaged citizens through social media during and in the aftermath of an EE. It also highlights the potential benefits of using social media for both governments and affected communities. Hurricane Sandy struck the US East Coast in 2012, during which both government agencies and citizens actively engaged in Twitter conversations, exchanging 132,922 tweets. The case study shows the critical contributions of citizens' information sharing with government agencies and their roles in (re-)distributing information to their Twitter followers. This specific form of co-production of public information services under duress seems to be essential for effective catastrophe response. It also demonstrates the potential benefits of government social media use.
The purpose of this study was to examine psychological factors that affect the transmission of rumor and criticism in social media during disasters. 40 students at Chiba University evaluated 10 rumor tweets and corresponding 10 criticism tweets that were posted in Twitter after the Japan March 11 Earthquake. Among some psychological factors, only importance was related to intended transmission of rumor. Surprisingly, accuracy and anxiety were not predictors of any transmission. Estimated transmission of criticisms was higher when its importance was high, while that of rumor did not vary according to importance. Interestingly, although participants estimated that criticisms were spread more than rumor, they intended to transmit rumors as much as criticisms.
The Great Eastern Japan Earthquake, which struck Japan on March 11, catastrophically affected all aspects of life: buildings, power plants, human life, etc. Moreover, it caused severe problems related to network infrastructure. We can ascertain the degree of network disorder from network traffic logs. Although we can infer what people did when the earthquake occurred on the Web from network traffic logs, we cannot know it precisely. Social media were used effectively during and after this earthquake, and they left a partial log revealing what people did on the Web during and after the earthquake. Such a log is one of the first logs of people's actions in a time of a catastrophic disaster. As described in this paper, we analyze Twitter logs and attempt to extract what happened in the emergency situation.
Online Social Networking Sites (SNS) are becoming extremely popular and can be employed in a variety of contexts. They permit the establishment of global relationships that are domain related or can be based on some general need shared by the participants. Emergency domain related websites, each with their own stated mission, are becoming widespread. Can a social network offer a solution to bringing emergency domain-related entities together as a 'one stop shop?' We propose to investigate whether the social network paradigm can be used to enable individuals and organizations to collaborate in mutually beneficial ways, in all stages of emergency management: mitigation, preparedness, response and recovery. Emergency management students were surveyed to examine the concept of social networks and their acceptance as a potential tool. The results of this exploratory research show overwhelming agreement that SNS should be considered a viable solution to the problems plaguing information dissemination and communications in the emergency domain.
We describe some first results of an empirical study describing how social media and SMS were used in coordinating humanitarian relief after the Haiti Earthquake in January 2010. Current information systems for crisis management are increasingly incorporating information obtained from citizens transmitted via social media and SMS. This information proves particularly useful at the aggregate level. However it has led to some problems: information overload and processing difficulties, variable speed of information delivery, managing volunteer communities, and the high risk of receiving inaccurate or incorrect information.
Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified? To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge.
Each day, we struggle to distinguish rumor from fact. Did the U.S. government blow up levees in New Orleans during Hurricane Katrina? Did American soldiers use night-vision goggles to spy on Iraqi women in Fallujah during the Iraqi War? These reports, taken from national and international media accounts, turned out to be false. In
Rumor Psychology: Social and Organizational Approaches, expert rumor researchers Nicholas DiFonzo and Prashant Bordia investigate how rumors start and spread, how their accuracy can be determined, and how rumors can be controlled, particularly given their propagation across media outlets and within organizations. Exactly what is rumor, and how does it differ from gossip? Even though these terms are commonly used interchangeably, they differ greatly in function and content. Whereas gossip serves to evaluate and shape the social network, rumor functions to make sense of an ambiguous situation or to help people adapt to perceived or actual threats. Why do people spread and believe rumors? Rumors attract attention, evoke emotion, incite involvement, and affect attitudes and actions. Rumor transmission is motivated by three broad psychological motivations--fact-finding, relationship enhancement, and self-enhancement--all of which help individuals and groups make sense in the face of uncertainty. Rumor is also closely entwined with a host of social and organizational phenomena, including social cognition, prejudice and stereotyping, interpersonal and intergroup relations, social influence, and organizational trust and communication. This book comes at an interesting time given the sociopolitical Zeitgeist, making the study of rumor accuracy, transmission, and propagation a high priority for the international intelligence community. It will also be of interest to social psychologists, organizational psychologists, and researchers in organizational communication, organizational behavior, human resource administration, and public relations personnel who regularly encounter rumors. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Twitter - a microblogging service that enables users to post messages ("tweets") of up to 140 characters - supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants retweet using different styles and for diverse reasons. We highlight how authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out retweeting as a conversational practice.
Twitter and other microblogs have rapidly become a significant means by which people communicate with the world and each other in near realtime. There has been a large number of studies surrounding these social media, focusing on areas such as information spread, various centrality measures, topic detection and more. However, one area which has not received much attention is trying to better understand what information is being spread and why it is being spread. This work looks to get a better understanding of what makes people spread information in tweets or microblogs through the use of retweeting. Several retweet behavior models are presented and evaluated on a Twitter data set consisting of over 768,000 tweets gathered from monitoring over 30,000 users for a period of one month. We evaluate the proposed models against each user and show how people use different retweet behavior models. For example, we find that although users in the majority of cases do not retweet information on topics that they themselves Tweet about as or from people who are “like them ” (hence anti-homophily), we do find that models which do take homophily, or similarity, into account fits the observed retweet behaviors much better than other more general models which do not take this into account. We further find that, not surprisingly, people’s retweeting behavior is better explained through multiple different models rather than one model. 1
Past and future objectives remain the same in crises, providing relevant communities collaborative knowledge systems to exchange information.
Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified? To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge.
Twitter was used to a great extent by government, media and individuals to obtain and exchange information real time during emergency. In ambiguous situation where information is crucial, some misinformation may creep in and spread around by retweet. This paper discusses on Twitter issues in emergency situation. A survey was conducted to investigate user's decision making after one read retweet messages in Twitter. As the result of the factor analyses, we grouped the 28 question items into three categories: 1) Desire to spread the retweet messages as it is considered important, 2) Mark the retweet messages as favorite using Twitter " Favorite " function, and 3) Search for further information about the content of the retweet messages.
Microblogging platforms are Web 2.0 services that represent a suitable environment for studying how information is propagated in social networks and how users can become influential. In this work we analyse the impact of the network features and of the users’ behaviour on the information diffusion. Our analysis highlights a strong relation between the level of visibility of a message in the flow of information seen by a user and the probability that the user further disseminates the message. In addition, we also highlight the existence of other latent factors that impact on the dissemination probability, correlated with the properties of the user that generated the message. Considering these results we define an information propagation model that generates information cascades (i.e. flows of messages propagated from user to user) whose statistical properties match empirical observations.
This paper examines the use of social media after the 2011 Great East Japan Earthquake. Based on media system dependency theory, the study focuses on the ways in which people used different types of social media to cope with a highly ambiguous situation created by the earthquake. A survey of Japanese university students revealed that the respondents used different forms of social media with different goals. Moreover, use of a particular social media type influenced the relative importance of social media for understanding developments relating to the Fukushima nuclear accident. Social media users and non-users also differed with regard to their use of other media. The implications of the current study in the context of ongoing research of the role of media in disaster situations are discussed.
Twitter was used to a great extent by government, media and individuals to obtain and exchange information real time during emergency. In ambiguous situation where information is crucial, some misinformation may creep in and spread around by retweet. This paper discusses on Twitter issues in emergency situation. A survey was conducted to investigate user’s decision making after one read retweet messages in Twitter. As the result of the factor analyses, we grouped the 28 question items into three categories: 1) Desire to spread the retweet messages as it is considered important, 2) Mark the retweet messages as favorite using Twitter “Favorite” function, and 3) Search for further information about the content of the retweet messages. © IFIP International Federation for Information Processing 2014.
Why do people spread disaster-related news in social media? To address this question, we analyzed people's tendency to share information discussing the Great East Japan Earthquake and the feelings that they experienced after reading the information in three conditions: when they were asked to think about themselves in a disaster center, when they were asked to think about another person, John, in a disaster center, and when they were not asked to take any perspective. A previous work showed that people who imagined themselves in a disaster center, Fukushima, Japan, were more likely to share related information. We successfully replicated the previous work and extended it by suggesting that feelings could predict the likelihood of information sharing. In this paper, we reported our new findings, proposed a model of information sharing during disaster response, and provided practical implications for advancing the effective use of social media technologies for crises management.
Social media provides a new and potentially rich source of information for emergency management services. However, extracting the relevant information from such streams poses a number of difficult challenges. In this short paper, we survey emergency management professionals to ascertain how social media is used when responding to incidents, the search strategies that they undertake, and the challenges that they face when using social media streams. This research indicates that emergency management professionals employ two main strategies when searching social media streams: keyword-centric and account-centric search strategies. Furthermore, current search interfaces are inadequate regarding the requirements of command and control environments in the emergency management domain, where the process of information seeking is collaborative in nature and needs to support multiple information seekers.
The Great East Japan Earthquake on March 11th, 2011 caused severe damage to the northern coast of the main island in Japan. Since then we have been working to help out the affected area in terms of IT support such as internetworking and providing PCs. Through our support activities we came across an interesting issue concerning collaboration with people from heterogeneous backgrounds. We call this problem disaster communications in this paper. We found that trust plays an important role in such communications. In this paper we report our experiences during our support activities and our findings.
In this paper, we examined information sharing behavior in social media when one was taking the perspective of self versus other. We found that imagining self in a disaster center, Fukushima, Japan, increased the likelihood of sharing crisis information relative to imagining another person, John, in the same place. People's intention to share crisis information by default, without being asked to take any perspective, paralleled the intention to share when taking another person’s perspective. Moreover, when the information was associated with negative feelings, such as worry or fear, it was more likely to be shared; when the information was perceived confusing or uninteresting, it was less likely to be shared.
Critical thinking is an important part of media literacy. It allows people to find facts among rumors and to inactivate false information. Such abilities are essential when social media is flooded with rumors during disaster response. We envision a social-technological system in which critical thinking is crowd-sourced: Individuals benefit from others' criticisms of false information, and the system inactivates the spread of false information. To test the plausibility of this system, we examined the effect of exposure to criticisms on people’s decision to spread rumors in social media. When people were exposed to criticisms before rumors, the proportion of responses aimed at stopping the spread of rumors was significantly larger than when people were exposed to rumors before criticisms. We identified some psychological factors that could explain this effect. Based on our results, we discuss practical implications for developing a social-technological system that harnesses the critical thinking of crowds.
EXAMINING THE "WAYS IN WHICH PEOPLE CAUGHT IN CRISIS SITUATIONS DEVELOP A WORKING ORIENTATION . . ." THE BOOK PRESENTS AN EXPLORATORY STUDY OF RUMORS AS INDICATIVE OF SOCIAL CHANGE. THE STUDY GIVES ATTENTION TO (1) ALTERNATIVE CONCEPTIONS OF RUMOR, (2) THE FAILURE OF FORMAL NEWS CHANNELS, (3) PROBLEM-SOLVING THROUGH DELIBERATION, (4) SUGGESTIBILITY AND BEHAVIORAL CONTAGION, (5) THE FORMATION OF POPULAR BELIEFS, (6) SOCIOLOGICAL THEORY OF RUMOR, AND (7) THE POLITICAL MANIPULATION OF RUMOR. A LIST OF CASE STUDIES IS INCLUDED IN THE APPENDIX. (28 P. REF.) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity.
Twitter, a microblogging service less than three years old, com- mands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. We have crawled the entire Twitter site and obtained 41:7 million user profiles, 1:47 billion social relations, 4; 262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effec- tive diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks (28). In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be sim- ilar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behav- ior and user participation. We have classified the trending topics based on the active period and the tweets and show that the ma- jority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1; 000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.
An Introduction to Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM)
- G Brown
Brown, G.: An Introduction to Confirmatory Factor Analysis (CFA)
and Structural Equation Modeling (SEM), available from
https://www.academia.edu/1680329/An Introduction to
Confirmatory Factor Analysis CFA and Structural Equation
Modeling SEM.
Tendency of Rumor and Correction Re-tweet on the Twitter during Disasters
- A Umejima
- M Miyabe
- E Aramaki
- A Nadamoto
Umejima, A., Miyabe, M., Aramaki, E. and Nadamoto, A.: Tendency
of Rumor and Correction Re-tweet on the Twitter during Disasters,
IPSJ SIG Technical Report (in Japanese) (2011).
A study on user's action towards retweet messages in Twitter
- N A Abdullah
- D Nishioka
- Y Tanaka
- Y Murayama
Abdullah, N.A., Nishioka, D., Tanaka, Y. and Murayama, Y.: A study
on user's action towards retweet messages in Twitter, The 31th Symposium on Cryptography and Information Security (SCIS 2014) (2014).
About Twitter, Inc., available from https
- Inc Twitter
Twitter, Inc.: About Twitter, Inc., available from https://about.twitter.
com/company.
Research on a Model for Decision Making in Retweet which caused Spreading of False Rumor in Emergencies
- M Mukai
Mukai, M.: Research on a Model for Decision Making in Retweet
which caused Spreading of False Rumor in Emergencies, Master Dissertation, Iwate Prefectural University (in Japanese) (2012).
Improvise news: A Sociological Study of Rumor, The Bobbs-Merrill Company
- T Shibutani
Shibutani, T.: Improvise news: A Sociological Study of Rumor, The
Bobbs-Merrill Company, Inc. New York (1966).
Mplus User's Guide, Seventh Edition
- L K Muthén
- B O Muthén
Muthén, L.K. and Muthén, B.O.: Mplus User's Guide, Seventh Edition, CA: Muthén & Muthén, Los Angeles (1998-2012).
Likert Scales, available from http
- D Bertram
Bertram, D.: Likert Scales, available from http://www.al-huda.net/
2012/PA/2014/topic-dane-likert.pdf.
Using Social Media in Emergencies: Smart Practices, available from <https://www.gov.uk/governmentUsing-social-media-in-emergencies-smart-tips
- Government
Emergency Response Information System Past, Present and Future
- M Turoff
- B Van De Walle
- S R Hiltz
Turoff, M., Van De Walle, B. and Hiltz, S.R.: Emergency Response Information System Past, Present and Future, Van de Walle, B., Turoff,
M., Hiltz, S.R. (Eds.) Information Systems for Emergency Management, New York, Advances in Management Information Systems,
Vol.16, pp.369-387 (2010).
Social media and SMS in the haiti earthquake
- J Dugdale
- B Van De Walle
- C Koeppinghoff
Dugdale, J., Van de Walle, B. and Koeppinghoff, C.: Social media and
SMS in the haiti earthquake, Proc. 21st Int. Conference on World Wide
Web (WWW '12 Companion), pp.713-714, ACM, NY, USA (2012).
The Domain of Emergency Management Information
- S R Hiltz
- B Van De Walle
- M Turoff
Hiltz, S.R., Van De Walle, B. and Turoff, M.: The Domain of Emergency Management Information, Van de Walle, B., Turoff, M., Hiltz,
S.R. (Eds.) Information Systems for Emergency Management, New
York, Advances in Management Information Systems, Vol.16, pp.3-20
(2010).
Why Do I Retweet It? An Information Propagation Model for Microblogs
- F Pezzoni
- J An
- A Passarella
- J Crowcroft
- M Conti
Pezzoni, F., An, J., Passarella, A., Crowcroft, J. and Conti, M.: Why
Do I Retweet It? An Information Propagation Model for Microblogs,
Jatowt, A., et al. (Eds.), Social Informatics 2013, LCNS 8238, pp.360-269 (2013).