Victoria Knieling’s research while affiliated with Emory University and other places

This paper makes four scientific contributions to the area of misinformation detection and analysis on digital platforms, with a specific focus on investigating how conspiracy theories, fake remedies, and false reports emerge, propagate, and shape public perceptions in the context of COVID-19. A dataset of 5,614 posts on the internet that contained misinformation about COVID-19 was used for this study. These posts were published in 2020 on 427 online sources (such as social media platforms, news channels, and online blogs) from 193 countries and in 49 languages. First, this paper presents a structured, three-tier analytical framework that investigates how multiple motives - including fear, politics, and profit - can lead to a misleading claim. Second, it emphasizes the importance of narrative structures, systematically identifying and quantifying the thematic elements that drive conspiracy theories, fake remedies, and false reports. Third, it presents a comprehensive analysis of different sources of misinformation, highlighting the varied roles played by individuals, state-based organizations, media outlets, and other sources. Finally, it discusses multiple potential implications of these findings for public policy and health communication, illustrating how insights gained from motive, narrative, and source analyses can guide more targeted interventions in the context of misinformation detection on digital platforms.

The work of this paper presents multiple novel findings from a comprehensive analysis of a dataset that includes the stress, anxiety, and depression levels experienced by 95 young adults computed using the Depression Anxiety Stress Scale (DASS). First, forage groups, 18-20, 21-25, and 26-30, average stress and anxiety levels were higher in females as compared to males. Second, for all these age groups, the percentagesof females who experienced a higher level of depression as compared to anxietyor stress were 15%, 16%, and 33.33%, respectively - indicating an increasing trend. However, such an increasing trend was not observed for males across different age groups. Third, for all these age groups, the percentages of females who experienced higher levels of stress as compared to anxiety or depression were 80%, 64%, and 66.67%, respectively. The pattern was observed to be different for males as for allthese age groups, the percentages of males who experienced a higher level of stressas compared to anxiety or depression were 41.66%, 59.09%, and 28.57%, respectively. Finally, Pearson’s correlation was used to analyze the nature of correlations between stress anxiety and depression for each of these diverse groups of young adults, which revealed multiple novel insights. For example, for the age group of 18-20, the correlation between the DASS Stress Score and the DASS Depression Score was observed to be statistically significant for males but not for females. For the age group of 26-30, the correlation between the DASS Anxiety Score and the DASS DepressionScore was observed to be statistically significant for females but not for males. In addition to this, for this age group, the correlation between the DASS Stress Score and the DASS Depression Score was also observed to be statistically significant for males but not for females.

The work of this paper presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. The dataset is available at https://dx.doi.org/10.21227/40s8-xf63. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. Finally, this paper also presents a list of open research questions that may be investigated using this dataset.

The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas—Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories Regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative. For the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative. For the videos belonging to the theme of Conspiracy Theories Regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative. For the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were neutral. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions for each topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels, such as Facebook and Twitter, were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of tweets and the correlation between the length of the video title and the number of Facebook posts were statistically significant.

The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas - Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative, for the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative, for the videos belonging to the theme of Conspiracy Theories regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative, and for the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were negative. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions per topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second-most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels such as Facebook and Twitter were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of Tweets as well as the correlation between the length of the video title and the number of Facebook posts was statistically significant.

During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females.

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.