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The 2016 US presidential election and media on Instagram: Who was in the lead?
Abstract
The present study is an effort to analyze the timing of media postings related to candidates Clinton and Trump on Instagram before and after the 2016 US presidential election. Hashtags are used to determine whether a posting was intended to support or oppose either candidate. We thus obtain four hourly time series: Clinton vs. Trump, supporters vs. opponents. Based on cross-wavelet analysis, we find that, at the 12-h period, Trump supporters were leading Trump opponents as well as Clinton supporters the days before the election, while Clinton opponents were often leading Clinton supporters: Trump supporters and Clinton opponents were eager to post media, while Trump opponents and Clinton supporters were sluggish. Considering election forecasts, our results come as a surprise.
... This paper addresses how municipalities use Instagram to engage with their citizens, because as a SM application, Instagram has not yet been widely examined in public administration literature. Although previous research (Bonsón & Bednárová, 2015;Cho, Phillips, Hageman, & Patten, 2009) has shown that visual content is more engaging than simple text, so entities not only in the private but also the public sector are focusing on the visual SM trend, Instagram being a pioneer in this regard (Schmidbauer, Rösch, & Stieler, 2018). ...
... Kim & Kim, 2018;Lee, Lee, Moon, & Sung, 2015;Moon, Lee, Lee, Choi, & Sung, 2016;Paramboukis, Skues, & Wise, 2016;Phua, Jin, & Kim, J. (Jay)., 2017;Pittman & Reich, 2016;Sheldon & Bryant, 2016). It turns out that the Instagram literature is almost non-existent when focused on its use by governments, beyond studies related to the use of Instagram during political campaigns under the umbrella of political communication (Russmann & Svensson, 2017;Schmidbauer et al., 2018;Towner & Muñoz, 2018;Turnbull-Dugarte, 2019). ...
... Previous literature has mainly investigated the cases of the United States and the Canada (Alhabash & Ma, 2017;Gruzd et al., 2018;B. Kim & Kim, 2018;Lalancette & Raynauld, 2019;Phua, Jin, & Kim, J. (Jay, 2017, Phua, Jin, & Kim, J. (Jay)., 2017Pittman & Reich, 2016;Schmidbauer et al., 2018;Towner & Muñoz, 2018). Therefore, there is a lack of literature exploring this phenomenon in other regions, such as Europe. ...
In this paper, we explore how local governments are using Instagram as a communication tool to engage with their citizens, using data from the municipalities of Andalusia (Spain). We seek to identify the determinants of local government use of Instagram, the determinants of activity in this channel and the determinants of citizen reactions in order to understand the influence of media types (picture, video or album) used in municipality posts, and to understand content type (what the post is about). Instaloader, an open source intelligence (OSINT) tool for Instagram, was applied. It made it possible to automatically extract all posts of the analysed municipalities (14,742 posts). These were later automatically analysed using R, an open source software. It was determined that of the 29 Andalusian local governments with the highest populations, only those that maintain an account on Instagram, totalling 17 municipalities (58.62%), would be part of the final analysis. Our findings demonstrate that when local governments have a high level of debt, they do not maintain and actively use Instagram accounts. We also found that quality of posts’ content is more important than quantity of followers, since there is no significant relationship between citizen reactions and the number of inhabitants of a municipality or the number of followers (audience), while there is a significant negative relationship between the number of posts (activity) and reactions. Our results also highlight that the level of reactions can be stimulated by certain media and content types.
... = TRUE, timelab = "", spec.period.axis = list(at = c (8,10,12,14,16)), graphics.reset = FALSE) abline(h = log(12)/log(2)) mtext(text = "12", side = 2, at = log(12)/log(2), las = 1, line = 0. 5) The result is shown in Figure 48. ...
... = list(lab = "wavelet power levels"), periodlab = "period (months)", spec.period.axis = list(at = c (8,10,12,14,16)), show.date = TRUE, timelab = "", graphics.reset ...
... For details, see Schmidbauer, Rösch and Stieler[14] ...
WaveletComp is an R package for continuous wavelet-based analysis of univariate and bivariate time series. Wavelet functions are implemented in WaveletComp such that a wide range of intermediate and final results are easily accessible. The null hypothesis that there is no (joint) periodicity in the series is tested via p-values obtained from simulation, where the model to be simulated can be chosen from a wide variety of options. The reconstruction, and thus filtering, of a given series from its wavelet decomposition, subject to a range of possible constraints, is also possible. WaveletComp provides extended plotting functionality — which objects should be added to a plot (for example, the ridge of wavelet power, contour lines indicating significant periodicity, arrows indicating the leading/lagging series), which kind and degree of smoothing is desired in wavelet coherence plots, which color palette to use, how to define the layout of the time axis (using POSIXct conventions), and others. Technically, we have developed vector- and matrix-based implementations of algorithms to reduce computation time. Easy and intuitive handling was given high priority.
Even though we provide some details concerning the mathematical foundation of the methodology implemented in WaveletComp, the present guide is not intended to give an introduction to wavelet analysis. The goal here is to give a series of constructed as well as real-world examples to illustrate the use and functionality of WaveletComp, with statistical arguments in mind.
... 10 The numbers of four comment categories of Trump and Clinton, supporters and opponents have been studied. 11 Usually classification accomplished by hashtags. The study made one day earlier U.S Presidential ballot demonstrated that Clinton's supporters were more than trump. ...
... Mohamed studied the Malaysian politician's storytelling. 11,12 In this, all the post of politicians, including video, picture, and text content, have been analyzed. The post is divided into six categories, and the results are compared to each other. ...
... Bilal et al. achieve significant accuracy in Pakistan's 2018 election results from Twitter data which, after extensive cleaning, can be used as valid factors to identify electoral intentions and potential outcomes at the ballot box [16]. Schmidbauer et al. describe how tracking hashtags on Instagram presented valuable results for predicting that Donald Trump would triumph over Hilary Clinton in the 2016 US election [17]. Chin and Wang apply predictive time series techniques to review the predictive value of social networks against the 2018 Taiwan election, indicating that incorporating Facebook into the analysis matrices used considerably increases the predictive value [18]. ...
This article presents the results of reviewing the predictive capacity of Google Trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analyzed were organized on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the Auto Regressive Integrated Moving Average (ARIMA) technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organizing the data: the linear series, the series transformed by moving average, and the series transformed by Hodrick–Prescott. The results indicate that the method offers the optimal predictive ability.
... Instagram was launched in October 2010, and at this point has the highest growth rate of any of the social media outlets. It had 10 million users one year after it was founded, but exceeded 500 million by June 2016, with about 100 million living in the US (Schmidbauer et al., 2018). Indeed, it could be speculated that Instagram was impacted by IRA activities in 2016 U.S. ...
The flow of misinformation and disinformation around the 2016 U.S. presidential election put the problem of “fake news” on the agenda all over the world. As a result, news organizations and companies have taken measures to reduce or eliminate the production and dissemination of fake news. Linguistic Inquiry and Word Count (LIWC) software was employed in the current study to examine 1,500 randomly selected tweets that were used to influence the 2016 U.S. presidential election. Results showed fake news are less likely to have analytical thinking. Moreover, both alt-Right troll accounts and alt-Left troll accounts posted fake news on Twitter. Lastly, Cluster analysis revealed that the fake news tweets are more likely to be retweeted and use fewer analytical thinking. APA Citation Padda, K. (2020). Fake news on Twitter in 2016 U.S. presidential election: a quantitative approach. The Journal of Intelligence, Conflict, and Warfare, 3(2), 18-45. https://journals.lib.sfu.ca/index.php/jicw/article/view/2374/1810.
... Schmidbauer et al. describe how tracking hashtags on Instagram presented valuable results for predicting that Trump would triumph over Clinton in the 2016 US election. (Schmidbauer et al., 2018). Chin and Wang apply predictive time series techniques to review the predictive value of social networks against the 2018 Taiwan election, indicating that incorporating Facebook into the analysis matrices used considerably increases the predictive value. ...
This article presents the results of reviewing the predictive capacity of Google trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analysed were organised on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the ARIMA technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organising the data: the linear series, the series transformed by moving average and the series transformed by Hodrick-Prescott. The result indicates that the method offers optimal pre-dictive ability.
... 117 Schmidbauer et al. Describen como el seguimiento de hashtags en Instagram presentó 118 resultados valiosos para preveer que Trump triunfaría sobre Clinton en 2016 para las 119 elecciones de Estados Unidos (Schmidbauer et al., 2018). Chin y Wang aplican técnicas 120 predictivas de series de tiempo para revisar el valor predictivo de redes sociales frente a 121 las elecciones en Taiwan en 2018, indicando que incorporar Facebook a las matrices de 122 análisis usadas aumenta considerablemente el valor predictivo (Chin & Wang, 2021). ...
El presente artículo presenta los resultados de revisar la capacidad predictiva de Google trends para 5 elecciones nacionales en Chile. Se revisaron los resultados electorales de las elecciones entre Michelle Bachelet y Sebastián Piñera en 2006, Sebastián Piñera y Eduardo Frei en 2010, Michelle Bachelet y Evelyn Matthei en 2013, Sebastián Piñera y Alejandro Guillier en 2017, y Gabriel Boric y José Antonio Kast en 2021. Las series de tiempo analizadas se organizaron a partir de búsquedas relativas entre las candidaturas, asistidas por el software R, con las librerías gtrendsR y forecast, principalmente. Con las series construidas, se hicieron pronósticos mediante la técnica ARIMA para revisar el peso de una opción presidencial sobre la otra. Los análisis ARIMA se hicieron sobre 3 modos de organizar los datos. El resultado indica que el método ofrece una capacidad predictiva óptima.
... In particular, focusing on the 2016 election, past research has examined information sharing (Schmidbauer, Rösch, and Stieler 2018;Zhang et al. 2018), information seeking (Beam, Hutchens, and Hmielowski 2018;Kahlor, Yang, and Liang 2018), and information processing (Yang, chu, and Kahlor 2019). Further, while there has been limited scholarship that examines presidential elections as a risk issue (Kahlor, Yang, and Liang 2018;Yang, chu, and Kahlor 2019), the innate uncertainties and potential negative consequences involved in an election warrant additional exploration (Tian et al. 2020). ...
During a presidential election cycle, voters consume information to alleviate uncertainty about the candidates and the process. As a result, elections offer an interesting (and somewhat novel) context for studying risk-related information behaviors. Here we argue that individuals avoid certain information, depending on the amount of risk they perceive and to what extent they experience risk-related affect. Based on national survey data collected one month prior to the 2016 U.S. presidential election (N = 512), we found that risk perception influenced information avoidance through affective responses to the different types of risks. Specifically, financial and political risk influenced avoidance through negative affective response to risk, while policy risk influenced avoidance through both positive and negative affective response. Thus, the mediation of affective responses varies by the type of perceived risks.
... However, acquiring spatial data from social networks in real-time is challenging since users generate a tremendous data volume (Christou, 2016). Therefore, a data analytics system is required to process data in an efficient way providing analysis of data generated by several networks in real-time, like Flickr, Foursquare, Twitter, etc. (Rehman et al., 2013;Donaire et al., 2014;Wang et al., 2015;Carvalho et al., 2017;Mittal et al., 2017;Komorowski et al., 2018;Schmidbauer et al., 2018;Liu et al., 2018). The process of data analysis consists of the acquisition, cleaning and storage of data, the application of analytics techniques on these data, and the querying and visualisation for presenting the results of the process (Floris and Campagna, 2014;Mahmud et al., 2016;Vassakis et al., 2019). ...
The existing practical research that social media and location intelligence in tourism is utilising a part of the possibilities that big data analytics can offer. Therefore, innovative big data analytics applications can provide new knowledge about behavioural data and perceptions in tourismdestinations. This research develops an innovative approach of leveraginggeotagged user-generated content in lo cation-based social networks (LBSNs) for tourism destinations. In contrast to the conventional spatio-temporalanalysis, valuable knowle dge is extracted about travellers’ behaviour,experiences, and opinions for tourist destinations. Our approach’s contribution has been demonstrated using user-generat ed content for the two largest islandsin the Mediterranean sea, Crete and Cyprus. The results of our study provide significant insights about the characteristics of the visitors in specific spots, their preferences, opinions and their tempo-spatial movements in tourism destinations – offering valuable information to tourism stakeholders for instantand effective strategic decision making that can lead to innovation and value creation.
... However, acquiring spatial data from social networks in real-time is challenging since users generate a tremendous data volume (Christou, 2016). Therefore, a data analytics system is required to process data in an efficient way providing analysis of data generated by several networks in real-time, like Flickr, Foursquare, Twitter, etc. (Rehman et al., 2013;Donaire et al., 2014;Wang et al., 2015;Carvalho et al., 2017;Mittal et al., 2017;Komorowski et al., 2018;Schmidbauer et al., 2018;Liu et al., 2018). The process of data analysis consists of the acquisition, cleaning and storage of data, the application of analytics techniques on these data, and the querying and visualisation for presenting the results of the process (Floris and Campagna, 2014;Mahmud et al., 2016;Vassakis et al., 2019). ...
... Kobayashi and Lambiotte find future retweet activity to be log-linear correlated with early tweet levels [14]. Outside of Twitter, analysis of Instagram posts immediately preceding the 2016 US presidential election showed a higher volume of user activity for Trump supporters as well as more intense pro-Trump activity at the sub-day time-scale when compared to pro-Clinton posts [15]. ...
The past decade has witnessed a marked increase in the use of social media by politicians, most notably exemplified by the 45th President of the United States (POTUS), Donald Trump. On Twitter, POTUS messages consistently attract high levels of engagement as measured by likes, retweets, and replies. Here, we quantify the balance of these activities, also known as “ratios”, and study their dynamics as a proxy for collective political engagement in response to presidential communications. We find that raw activity counts increase during the period leading up to the 2016 election, accompanied by a regime change in the ratio of retweets-to-replies connected to the transition between campaigning and governing. For the Trump account, we find words related to fake news and the Mueller inquiry are more common in tweets with a high number of replies relative to retweets. Finally, we find that Barack Obama consistently received a higher retweet-to-reply ratio than Donald Trump. These results suggest Trump’s Twitter posts are more often controversial and subject to enduring engagement as a given news cycle unfolds.
... However, despite the pluralism of opinions regarding the efficacy of Social Media platforms, Twitter and Instagram remain the objects of research in the context of identifying, predicting, and changing electoral preferences in different countries. Several works present an analysis of the content of election information and election forecasts on Instagram (Schmidbauer, 2018), the use of Twitter to change electoral preferences (Grover, 2019). Through social media analysis, the researchers reveal the impact of the nature of social media discussions through acculturation of ideologies and polarization of voter preferences on voter behavior during elections. ...
... In more general studies, hashtags serve as a filter to receive material for content analysis. For example, in order to receive access to political debates of Clinton/Trump supporter during the 2016 US presidential election, Schmidbauer et al. [30] analyzed Instagram postings collected through 16 hashtags related to the topic. ...
Social media enables conversations mediated through documents as texts, audio, images, or videos. Likewise, hashtags became an essential medium for social media communication. Instagram is well-known as one of the current platforms for hashtagging. This exploratory study investigates how hashtags used on Instagram became established in respect of self-creation and best practices or tools. The analysis is based on data obtained from an online survey (N = 1,006) of Instagram users. 55.7% of the respondents use hashtags on Instagram. Only self-created hashtags are assigned by 41.4%, whereas 58.6% are (sometimes) inspired by others. Best practices and tools based on friends/other users or Instagram functions are more frequently used in contrast to offers from influencers or third-parties (e.g. guides, hashtag-sets). Furthermore, the majority does not intentionally use false hashtags. This study enables a first overview of the Instagram users’ hashtagging creation behavior and selection process.
Download the full paper here: http://hdl.handle.net/10125/64080
... In comparison to other major candidates in the 2016 Republican primaries and the 2016 Presidential election, Donald Trump entered the race with a lack of support from politicians in his own party [1][2][3], the mainstream media [4], and large private donors [5], all of which have traditionally been seen as necessary for a successful campaign [3,6]. Since Trump's unprecedented success, many studies have attempted to explain how he overcame these obstacles to become the 45th President of the United States [7][8][9][10][11][12]. On Twitter, Trump gave at least some of the credit to his social media presence: The claim that social media was integral to Trump's campaign has been supported by several independent studies [13][14][15][16][17][18][19]. ...
Twitter was an integral part of Donald Trump’s communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.
... Guan et al. [14] investigated on knowledge contribution behavior of users and participation in online question & answer communities. The study on Presidential election of US 2016 has been presented by Sachmidbauer et al. [51]. ...
This article proposes novel frameworks of SentiVerb and Spell Checker system, which extracts the reaction, mood, and opinion of users from social media text (SMT). The opinion of users is extracted from their written text on social media such as comments, tweets, blogs, feedbacks etc. and are classified as positive or negative opinion based on sentiment score of SMT using dictionary-based approach and a binary classifier. The dictionary-based approach uses opinion verb dictionary (OVD) to extract the sentiment of opinion verbs present in SMT. This OVD contain only opinion verbs along with their sentiment score. The various steps of the framework such as lower-case conversion, tokenization, spell checker, Part-of-Speech tagging, stop word elimination, stemming, sentiment score calculation, and classification of SMT has been discussed. A new concept of threshold negative parameter is first time introduced in this article. In the experiment, the proposed SentiVerb system’s performance is evaluated on three datasets such as Facebook comments on goods and services tax (GST) implementation in India, tweets on the debate between former president of USA Mr. Barack Obama and Mr. John McCain, and the movie reviews. Consequently, the implementation of the proposed SentiVerb system using rule-based classifier (RBC) gives the best performance result in term of accuracy with 82.5% on GST comments and 79.18% on Obama-McCain debate, which is better than the existing algorithms on the social issues related domain dataset(s). Also, system performance (accuracy of 71.3%) is better than others results on standard movie dataset.
... Researchers observed political debates on Instagram. For example, Schmidbauer, Rösch, and Stieler [37] examined postings collected by crawling 16 hashtags related to Clinton/Trump supporters and opponents during the 2016 US presidential election. 9,000 multilingual hashtags were studied by Lee and Chau [38] in the context of the Umbrella Movement in Hong Kong in 2014. ...
Instagram is widely known and used as a social media application for visual content. In order to categorize and describe their posted content as well as to make it retrievable, users can assign hashtags to each posting. What kind of hashtags do female and male Instagram users assign to their picture postings? Which differences and similarities exist? This study analyzes gender-specific image tagging behavior on Instagram. Therefore, a content analysis of, in total, 14,951 hashtags from 1,000 Instagram pictures (respectively 500 pictures posted by female and male users) was performed. The subjects of the 1,000 Instagram pictures belong to overall ten picture categories (100 pictures per category): Activity, Architecture, Art, Captioned Photo, Fashion, Food, Friends, Landscape, Pet, and Selfie. Seven categories exist for the coding of the hashtags: Content-relatedness, Emotiveness, Fakeness, “Insta”-Tags, Isness, Performativeness, and Sentences. On average, women assigned 14 hashtags to their postings, whereas men used one hashtag more. For both genders, hashtags belonging to the category Content-relatedness were the most used (over 55% of assigned hashtags). Second most assigned (over 17%) were Isness related hashtags. Generally, females used slightly more emotional hashtags, whereas men assigned Isness and “Insta”-Tags in a higher frequency than females. “Insta”-Tags were assigned in high frequencies (over 22%) to Pet pictures by both genders. With under 2%, females and males did not use many Sentences hashtags. As a chi-square test of independence shows, there exists a small statistical association between hashtag and picture categories for male and female Instagram users, respectively.
... The global datasphere has been predicted to grow from 16.1 zettabytes (ZB) generated in 2016 to 163 ZB by 2025, unlocking unique user experiences and new opportunities. More than a quarter of this data will be in real-time and more than 95% of this will be produced by real-time Internet of Things (IoT) devices (Reinsel et al. 2017). Big data can be characterized by seven Versus: (1) volume, large datasets generated through technologies, like IoT and user-generated content (UGC) from online social media platforms, (2) variety, the diversity of data sources and formats, (3) variability, data with meaning that can vary significantly in context, (4) velocity, high speed data generation through the growth of interconnected devices generating data in real-time, (5) veracity, data reliability, as data is worthless if it's not accurate, (6) visualization, visual representation of data and information in a way that is easily readable, accessible and understandable (Vassakis et al. 2017). ...
Social media networks are a resource for valuable knowledge about tourist destinations through the collection of data by Location-Based Social Networks (LBSN). A major problem is the lack of knowledge in respect to the visitors’ views about a destination, as well as the fact that the visitors’ behavior needs and preferences are not visible. Many enterprises and local authorities are still using traditional methods for acquiring knowledge to make strategic decisions, by collecting data from questionnaires. Nonetheless, this process, despite its benefits, is short-lived and the number of the participants is small compared to the number of visitors. This chapter discusses a methodology for the extraction, association, analysis, and visualization of data derived from LBSNs. This provides knowledge of visitor behaviors, impressions and preferences for tourist destinations. A case study of Crete in Greece is included, based upon visitors’ posts and reviews, nationality, photos, place rankings, and engagement.
... Services such as Regram also allow users to share images from other Instagram feeds, similar to the retweeting function on Twitter, or reblogging on Tumblr. As a result, Instagram is increasingly also a site of media and news sharing (see Schmidbauer et al. 2018). ...
The image-sharing social media platform Instagram has become a site for political discourse that combines visual and textual elements. These political conversations often take place in the form of memes, or popular graphic sentiments intended for redistribution. Scholars have identified memes as markers of subcultural knowledge that may be used to reinforce beliefs and norms, define social boundaries, and disparage outgroups.
Gun rights activism in the United States has intensified in an increasingly partisan environment. We examined memes shared via Instagram to popular gun culture hashtags between June 2016 and February 2018. Insofar as memes act as vehicles for subcultural beliefs and values, here they may be seen as representing multiple realities from the perspective of Second Amendment enthusiasts: descriptions of their perceived reality, justifying the need for guns and conservative political positions; the construed reality of what the meme creators and sharers believe to be liberal attitudes; and the aspirational reality of a world that offers unobstructed support for their priorities. In other words, these memes can collectively be said to represent both the truth about the world, as seen by these users, and cultural messages to set power differentials and identity boundaries with Others.
Multicomponent environmental models have increasingly found their way into archaeological research. Mostly, these models aim to understand human patterns as a result of past climatic and environmental conditions over long-term periods. However, major limitations are the low spatial and temporal resolution of the environmental data, and hence the rather static model output. Particular challenges are thus the number of chosen variables, the comprehensiveness of the explanatory parameters, and the integration of socio-cultural decision-making into the model. Here, we present a novel approach to generate annually resolved landcover variability using a broad variety of climatic, geological, hydrological, topographical, and dendrochronological data composites (Palmer Drought Severity Index (PDSI)). We analyze land-use and settlement capacity and vulnerability to estimate the socio-cultural transformation processes at Basel (Switzerland) during the Late Antiquity and the Early Middle Ages. Our results highlight the potential of the PDSI to predict local river run-off behavior from catchment analyses. The model enables to trace landcover as well as socio-cultural response to climatic variability and subsequent adaptation to trends in environmental vulnerability. This approach further helps to understand population dynamics in the periphery of the Roman administrative boundaries and to revise traditional archaeological narratives of large-scale population replacements during the so-called Migration Period.
Multicomponent environmental models have increasingly found their way into archaeological research. Mostly, these models aim to understand human patterns as a result of past climatic and environmental conditions over long-term periods. However, major limitations are the low spatial and temporal resolution of the environmental data, and hence the rather static model output. Particular challenges are thus the number of chosen variables, the comprehensiveness of the explanatory parameters, and the integration of socio-cultural decision-making into the model. Here, we present a novel approach to generate annually resolved landcover variability using a broad variety of climatic, geological, hydrological, topographical, and dendrochronological data composites (Palmer Drought Severity Index (PDSI)). We analyze land-use and settlement capacity and vulnerability to estimate the socio-cultural transformation processes at Basel (Switzerland) during the Late Antiquity and the Early Middle Ages. Our results highlight the potential of the PDSI to predict local river runoff behavior from catchment analyses. The model enables to trace landcover as well as socio-cultural response to climatic variability and subsequent adaptation to trends in environmental vulnerability. This approach further helps to understand population dynamics in the periphery of the Roman administrative boundaries and to revise traditional archaeological narratives of large-scale population replacements during the so-called Migration Period.
This study focuses on the use of Instagram by tourism companies. Specifically, it aims to analyze how Instagram empowers individuals over corporations in the digital dialogue, thus balancing the information asymmetry between corporations and stakeholders. Four categories of metrics have been collected to analyze the use of Instagram by companies: presence, impact, conversation, and influence. Additionally, OLS regressions have been performed to identify potential explanatory factors to explain the different behavior of each firm and its corresponding communities. While the use of Instagram by the selected firms is still scarce, significant results are as follows: corporations are using Instagram as an additional channel in their current communication; some explanatory factors account for significant differences in countries, size, and industries; and users are somehow reactive to the stream of pictures and texts disclosed by firms. The presence and impact of companies on Instagram are a highly important source for driving stakeholders’ conversation within the digital arena.
River discharge/flow exerts fundamental driving effects on the river ecosystem and larval abundance dynamics. In the subtropical Pearl River, China, the species‐specific effects of river discharge on the occurrence of grass carp larvae remain unknown. We performed cross wavelet analysis, using data on the daily density of larvae collected from 2006 to 2013 at a fixed site downstream in the Pearl River, to explore the temporal correlation between river discharge/water temperature and larval density. We further used the conditional random forest and generalised additive models to determine relevant environmental effects on their relationship. Grass carp larvae appeared from late April to early October, but mainly during June and July. River discharge, but not water temperature, showed significant coherence and in‐phase relationship with larval density. For discharge in significant coherence with larval density, it ranged from 4740 to 28 700 m3/s, with peak discharge from 4900 to 30 900 m3/s, and the discharge fluctuation state from ‐16 d to 10 d (where ‐ indicates the falling stage and + the rising stage). Maximum daily temperature/water temperature, ammonia‐nitrogen concentration, peak discharge and discharge contributed the most to the variations in coherence and phase relationships between discharge and larval densities over the short timescale (< 32 d), and accounted for 22.4% of coherence variation, 19.7% of in‐phase degrees, and 15% of variation in phase lagging. These suggest that a combination of ecological regulation and water environment control would be efficient to promote the recruitment of grass carp and relevant freshwater fish species.
This work shows how Internet data can be aggregated to track political candidates’ performance during a presidential campaign. The ‘wisdom of crowds’ theory is here exploited to a fuller extent using a unique combination of data sources and methodology. This is first done by taking medium-level aggregations from applications such as BetFair, InTrade, PredictIt, Twitter, Facebook, MySpace, YouTube, Instagram, Google, FiveThirtyEight, and then by synthesizing via factor analysis a qualitative measure of popularity for both candidates, while controlling for the intensity of electoral discussions. For two different elections, the methodology extracts popularity functions that closely reflect popularity swings occurring during/shortly after the presidential debates and other campaign-related events. The model presents itself as a cheaper and more accurate alternative to electoral polling being based on aggregate, anonymous data and voter's actions; for the same reason, it has the potential to address the “Shy Trump Supporters” bias. The model outperforms political betting markets and established platforms such as RealClearPolitics, FiveThirtyEight and Twitter, in addition to the factor model's original variables.
In the last few years the micro-blogging platform Twitter has played a significant role in the communication of civil uprisings, political events or natural disasters. One of the reasons is the adoption of the hashtag, which represents a short word or phrase that follows the hash sign (). These semantic elements captured the topics behind the tweets and allowed the information flow to bypass traditional social network structure. The hashtags provide a way for users to embed metadata in their posts achieving several important communicative functions: they can indicate the specific semantic domain of the post, link the post to an existing topic, or provide a range of complex meanings in social media texts. In this paper, Generalized nets are applied as a tool to model the structural characteristics of a hashtag linguistic network through which possible communities of interests emerge, and to investigate the information propagation patterns resulting from the uncoordinated actions of users in the underlying semantic hashtag space. Generalized nets (GN) are extensions of the Petri nets by providing functional and topological aspects unavailable in Petri nets. The study of hashtag networks from a generalized nets perspective enables us to investigate in a deeper manner each element of the GN, substituting it with another, more detailed network in order to be examined in depth. The result is an improved understanding of topological connections of the data and the ability to dynamically add new details to expand the network and as a result discover underlying structural complexities unable to be discovered through traditional network analysis tool due to the prohibitive computational cost. Analysis is performed on a collection of Tweets and results are presented.
Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner / creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.
The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including:graph partitioning, centrality, assortativity, hierarchy and structural balance. The analysis yields various patterns. First, weobserve that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat camp; the overall campaign was much focused on economy and rights; the Republican Party (Grand Old Party or GOP) is the most divisive subject in the campaign, and is portrayed more negatively than the Democrats; and, overall, the media reported positive statements more frequently forthe Democrats than the Republicans. This is the first study in which political positions are automatically extracted and derived froma very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts.
During the race for the presidency in 2007-2008, one of the editors of this special issue of Mass Communication & Society spent a month reading Barack Obama's official campaign MySpace page (Perlmutter, 2008). Its most notable feature was the posting of comments by thousands of people, not all of them supporters of the Illinois senator's bid for the White House. ©Mass Communication & Society Division of the Association for Education in Journalism and Mass Communication.
The third-person perception is the tendency for people to believe others are more affected by media messages than they are themselves. Analysis of a probability sample telephone survey revealed a significant relationship between having voted in the 2004 presidential election and third-person perception level. This empirical evidence supports a link between past voting behavior and the third-person perception, which expands third-person perception and behavioral domains beyond censorship issues. Republicans were also found to have a significantly greater third-person perception level than Democrats.
This study looks at student Facebook groups supporting the 2008 presidential candidates, John McCain and Barack Obama, from largest land-grant universities in seven battleground states. The findings of a content analysis of wall posts show that students are using Facebook to facilitate dialog and civic political involvement. In opposition to pro-McCain groups, pro-Obama groups have wider time frame coverage and demonstrate substantively higher site activity. Political discussions related to the political civic process, policy issues, campaign information, candidate issues, and acquisition of campaign products dominate across groups and election seasons. An examination of the content of wall posts based on the four categories of the Michigan Model of voting behavior (partisanship, group affiliation, candidate image, and political/campaign issues) reveals that in the primary season, pro-Obama groups focus mostly on short-term topics (candidate image and campaign issues), whereas pro-McCain groups focus mostly on long-term topics (partisanship and group affiliation). The overall findings of this study suggest that youth online communities actively follow campaigns and post comments that foster the political dialog and civic engagement.
The cross wavelet transform (XWT) is a powerful tool for testing the proposed connections between two time series. Because of XWT’s skeletal structure, which is based on the wavelet transform, it is suitable for the analysis of non-stationary periodic signals. Recent work has shown that the power spectrum based on the wavelet transform can produce a deviation, which can be corrected by choosing a proper rectification scale. In this study, we show that the standard application of the XWT can also lead to a biased result. A corrected version of the standard XWT was constructed using the scale of each series as normalizing factors. This correction was first tested in an artificial example involving two series build from combinations of two harmonic series of different amplitudes and frequencies. The standard XWT applied to this example produce a biased result, whereas the correct result is obtained with used of the proposed normalization. This analysis was then applied to a real geophysical situation with important implications to climate modulation on the northwestern Brazilian coast. The linkage between the relative humidity and the shortwave radiation measurements, obtained from the 8°S 30°W ATLAS buoy of the Southwestern Extension of the Prediction and Research moored Array in the Tropical Atlantic Project (PIRATA-SWE), was explored. The analysis revealed the importance of including the correction in order to not overlook any possible connections. The requirements of incorporating this correction in the XWT calculations are emphasized.
Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of "pulsing" the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow.
Morlet et al (1982, this issue) showed the advantages of using complex values for both waves and characteristics of the media. We simulated the theoretical tools we present here, using the Goupillaud-Kunetz algorithm. Now we present sampling methods for complex signals or traces corresponding to received waves, and sampling methods for complex characterization of multilayered or heterogeneous media. Regarding the complex signals, we present a two-dimensional (2-D) method of sampling in the time-frequency domain using a special or "extended" Gabor expansion on a set of basic wavelets adapted to phase preservation. Such a 2-D expansion permits us to handle in a proper manner instantaneous frequency spectra. We show the differences between "wavelet resolution" and "sampling grid resolution." We also show the importance of phase preservation in high-resolution seismic. Regarding the media, we show how analytical studies of wave propagation in periodic structured layers could help when trying to characterize the physical properties of the layers and their large scale granularity as a result of complex deconvolution. Analytical studies of wave propagation in periodic structures are well known in solid state physics, and lead to the so-called "Bloch waves." The introduction of complex waves leads to replacing the classical wave equation by a Schrödinger equation. Finally, we show that complex wave equations, Gabor expansion, and Bloch waves are three different ways of introducing the tools of quantum mechanics in high-resolution seismic (Gabor, 1946; Kittel, 1976; Morlet, 1975). And conversely, the Goupillaud-Kunetz algorithm and an extended Gabor expansion may be of some use in solid state physics.
Wavelet analysis is becoming more popular in the Economics discipline. Until recently, most works have made use of tools associated with the Discrete Wavelet Transform. However, after 2005, there has been a growing body of work in Economics and Finance that makes use of the Continuous Wavelet Transform tools. In this article, we give a self-contained summary on the most relevant theoretical results associated with the Continuous Wavelet Transform, the Cross-Wavelet Transform, the Wavelet Coherency and the Wavelet Phase-Difference. We describe how the transforms are usually implemented in practice and provide some examples. We also introduce the Economists to a new class of analytic wavelets, the Generalized Morse Wavelets, which have some desirable properties and provide an alternative to the Morlet Wavelet. Finally, we provide a user friendly toolbox which will allow any researcher to replicate our results and to use it in his/her own research.
Instagram is the fastest growing social network site globally. This study investigates motives for its use, and its relationship to contextual age and narcissism. A survey of 239 college students revealed that the main reasons for Instagram use are "Surveillance/Knowledge about others," "Documentation," "Coolness," and "Creativity." The next significant finding was a positive relationship between those who scored high in interpersonal interaction and using Instagram for coolness, creative purposes, and surveillance. Another interesting finding shows that there is a positive relationship between high levels of social activity (traveling, going to sporting events, visiting friends, etc.) and being motivated to use Instagram as a means of documentation. In reference to narcissism, there was a positive relationship between using Instagram to be cool and for surveillance. Theoretical contributions of this study relate to our understanding of uses and gratifications theory. This study uncovers new motives for social media use not identified in previous literature.
When defined in terms of social identity and affect toward copartisans and opposing partisans, the polarization of the American electorate has dramatically increased. We document the scope and consequences of affective polarization of partisans using implicit, explicit, and behavioral indicators. Our evidence demonstrates that hostile feelings for the opposing party are ingrained or automatic in voters' minds, and that affective polarization based on party is just as strong as polarization based on race. We further show that party cues exert powerful effects on nonpolitical judgments and behaviors. Partisans discriminate against opposing partisans, doing so to a degree that exceeds discrimination based on race. We note that the willingness of partisans to display open animus for opposing partisans can be attributed to the absence of norms governing the expression of negative sentiment and that increased partisan affect provides an incentive for elites to engage in confrontation rather than cooperation.
1. Introduction to wavelets 2. Review of Fourier theory and filters 3. Orthonormal transforms of time series 4. The discrete wavelet transform 5. The maximal overlap discrete wavelet transform 6. The discrete wavelet packet transform 7. Random variables and stochastic processes 8. The wavelet variance 9. Analysis and synthesis of long memory processes 10. Wavelet-based signal estimation 11. Wavelet analysis of finite energy signals Appendix. Answers to embedded exercises References Author index Subject index.
A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El NiñoSouthem Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmöller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of changes in ENSO variance on interdecadal timescales. Using new datasets that extend back to 1871, the Niño3 sea surface temperature and the Southern Oscillation index show significantly higher power during 1880-1920 and 1960-90, and lower power during 1920-60, as well as a possible 15-yr modulation of variance. The power Hovmöller of sea level pressure shows significant variations in 2-8-yr wavelet power in both longitude and time.
Twitter as a new form of social media potentially contains useful information
that opens new opportunities for content analysis on tweets. This paper
examines the predictive power of Twitter regarding the US presidential election
of 2012. For this study, we analyzed 32 million tweets regarding the US
presidential election by employing a combination of machine learning
techniques. We devised an advanced classifier for sentiment analysis in order
to increase the accuracy of Twitter content analysis. We carried out our
analysis by comparing Twitter results with traditional opinion polls. In
addition, we used the Latent Dirichlet Allocation model to extract the
underlying topical structure from the selected tweets. Our results show that we
can determine the popularity of candidates by running sentiment analysis. We
can also uncover candidates popularities in the US states by running the
sentiment analysis algorithm on geo-tagged tweets. To the best of our
knowledge, no previous work in the field has presented a systematic analysis of
a considerable number of tweets employing a combination of analysis techniques
by which we conducted this study. Thus, our results aptly suggest that Twitter
as a well-known social medium is a valid source in predicting future events
such as elections. This implies that understanding public opinions and trends
via social media in turn allows us to propose a cost- and time-effective way
not only for spreading and sharing information, but also for predicting future
events.
This study examines the ways in which online media influenced political disaffection among young adults during the 2008 presidential election campaign. The effects of social media attention, online expression, and traditional Internet sources on political cynicism, apathy, and skepticism were evaluated using data from an online survey of college students. Results show that attention to social media for campaign information is positively related to cynicism and apathy and negatively related to skepticism. Online expression has a positive effect on skepticism. Implications are discussed for the role of social media in bringing a historically disengaged demographic into the political process.
Despite research to the contrary, the general public and a significant number of politicians are convinced the U.S. news media have a liberal and pro-Democratic bias. To understand why many people believe the media ha ve such biases, this study tested whether such a perception is related to an observer's own partisan and ideological positions. Findings based on two large national surveys suggest that audiences' ideologies and partisanships affect how they view the media. Strong conservatives and Republicans are more likely to distrust the news media, whereas the best predictor of a media bias perception is political cynicism.
This study examined college students' use of online media for political purposes in the 2008 election. Social media attention, online expression, and traditional Internet attention were assessed in relation to political self-efficacy and situational political involvement. Data from a Web survey of college students showed significant positive relationships between attention to traditional Internet sources and political self-efficacy and situational political involvement. Attention to social media was not significantly related to political self-efficacy or involvement. Online expression was significantly related to situational political involvement but not political self-efficacy. Implications are discussed for political use of online media for young adults.
Although Facebook is primarily known for building and maintaining relationships, the 2008 presidential election highlighted this social networking website as a viable tool for political communication. In fact, during primary season until Election Day in 2008, Facebook users created more than 1,000 Facebook group pages that focused on Barack Obama and John McCain. Using quantitative content analysis, the primary purpose of this study was to assess how both John McCain and Barack Obama were portrayed across these Facebook groups. Results indicated that group membership and activity levels were higher for Barack Obama than for John McCain. Overall, Barack Obama was portrayed more positively across Facebook groups than John McCain. In addition, profanity, racial, religious, and age-related language were also coded for and varied with regard to how each candidate was portrayed. Theoretical and practical implications are discussed.
I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods.To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies.
From experimental studies in digital processing of seismic reflection data, geophysicists know that a seismic signal does vary in amplitude, shape, frequency and phase, versus propagation time. To enhance the resolution of the seismic reflection method, we must investigate these variations in more detail. We present quantitative results of theoretical studies on propagation of plane waves for normal incidence, through perfectly elastic multilayered media. As wavelet shapes, we use zero-phase cosine wavelets modulated by a Gaussian envelope and the corresponding complex wavelets. A finite set of such wavelets, for an appropriate sampling of the frequency domain, may be taken as the basic wavelets for a Gabor expansion of any signal or trace in a two-dimensional (2-D) domain (time and frequency). We can then compute the wave propagation using complex functions and thereby obtain quantitative results including energy and phase of the propagating signals. These results appear as complex 2-D functions of time and frequency, i.e., as "instantaneous frequency spectra." Choosing a constant sampling rate on the logarithmic scale in the frequency domain leads to an appropriate sampling method for phase preservation of the complex signals or traces. For this purpose, we developed a Gabor expansion involving basic wavelets with a constant time duration/mean period ratio. For layered media, as found in sedimentary basins, we can distinguish two main types of series: (1) progressive series, and (2) cyclic or quasi-cyclic series. The second type is of high interest in hydrocarbon exploration. Progressive series do not involve noticeable distortions of the seismic signal. We studied, therefore, the wave propagation in cyclic series and, first, simple models made up of two components (binary media). Such periodic structures have a spatial period. We present synthetic traces computed in the time domain using the Goupillaud-Kunetz model of propagation for one-dimensional (1-D) synthetic seismograms. Three different cases appear for signal scattering, depending upon the value of the ratio wavelength of the signal/spatial period of the medium. (1) Large wavelengths The composite medium is fully transparent, but phase delaying. It acts like an homogeneous medium, with an "effective velocity" and an "effective impedance." (2) Short wavelengths For wavelengths close to twice the spatial period of the medium, the composite medium strongly attenuates the transmission, and superreflectivity occurs as counterpart. (3) Intermediate wavelengths For intermediate values of the frequency, velocity dispersion versus frequency appears. All these phenomena are studied in the frequency domain, by analytic formulation of the transfer functions of the composite media for transmission and reflection. Such phenomena are similar to Bloch waves in crystal lattices as studied in solid state physics, with only a difference in scale, and we checked their conformity with laboratory measurements. Such models give us an easy way to introduce the use of effective velocities and impedances which are frequency dependent, i.e., complex. They will be helpful for further developments of "complex deconvolution." The above results can be extended to quasi-cyclic media made up of a random distribution of double layers. For signal transmission, quasi-cyclic series act as a high cut filter with possible time delay, velocity dispersion, and "constant Q" type of law for attenuation. For signal reflection they act as a low cut filter, with possible superreflections. These studies could be extended to three-dimensional (3-D) binary models (grains and pores in a porous reservoir), in agreement with well-known acoustic properties of gas reservoirs (theory of bright spots). We present some applications to real well data.
This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power spectrum should be the transform coefficient squared divided by the scale it associates. Such adjusted wavelet power spectrum results in a substantial improvement in the spectral estimate, allowing for a comparison of the spectral peaks across scales. The improvement is validated with an artificial time series and a real coastal sea level record. Also examined is the previous example of the wavelet analysis of the Niño-3 SST data.
Fake accounts still plague Instagram despite purge, study finds
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Predicting election trends with Twitter: Hillary Clinton versus Donald Trump
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Not all fair probabilistic schedulers are equivalent
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What we Instagram: A first analysis of Instagram photo content and user types
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