Generative artificial intelligence (AI) has gained a great deal of recent attention with the release of Chat GPT 4. It has been praised for its ability to generate human-like responses but has perhaps faced even more criticism over potential concerns for biased responses, misinformation, and generation of harmful or inappropriate content. Chat GPT utilizes large sources of data to curate responses to all kinds of questions. The generative AI models are designed to be objective and avoid any sort of bias in their output. However, in the age of misinformation, social media, user-generated content and the 24-hour news cycle, biased information has never been more plentiful. This paper investigates the possibility of biased responses produced by Chat GPT 4 utilizing public data from biased media sources through Support Vector Machines. We find Chat GPT tends to have bias in its responses.

The well documented mental health crisis among preteens and teenagers worldwide is often believed to be intertwined with the increasing ubiquity of social media services, a belief borne out by numerous findings in the literature. However, the literature has not kept pace with recent developments in the social-media sphere, such as the rise of TikTok and the decision by Instagram to pivot to video. This paper aims to help alleviate this deficiency, examining whether a correlation exists between college students' social media usage and mental health concerns. Using a random sample of 254 undergraduate college students at a mid-Atlantic university, we find (as evidenced by Spearman's rank correlation coefficients) that while there is a correlation between "fear of missing out (FoMO)" and social media use, the correlation is not strong, suggesting that social media use alone cannot explain observed mental health outcomes. We support previous literature regarding correlations between personality and social media use and extend it with additional measures of use to include FoMO and loneliness. We find that loneliness is weekly and inversely correlated with Instagram and Snapchat use and personality traits moderate use. We speculate that the shift in social media from peer-to-peer text to short video may be driving divergence from previous findings in the literature, lessening risk, but suggesting that additional research is needed to confirm our conclusions.

Initial scientific studies suggest the spread of extreme content, “the COVID-19 infodemic,” likely plays a crucial role in news spread about the “the COVID-19 pandemic.” In this paper, we quantify the evolution of polarization and engagement in YouTube social networks about public-health interventions for COVID-19. Although YouTube is a major information and news source with high engagement with younger populations, the platform is not widely researched in social network analysis. Discussions about coronavirus on social media can influence how individuals interpret news about the disease and affect their compliance with various non-pharmaceutical interventions. We compare coronavirus video content by identifying three subgroups of public-health intervention-related videos: individual interventions, government interventions, and medical interventions, as well as seven video title narratives. The polarization index measures the level of agreement with the video content using votes: likes and dislikes. The engagement index measures the level of user interaction by comparing views, votes, and number of comments. We observe that over time, engagement for the intervention video subgroups has increased whereas the diffusion for other non-intervention videos has decreased, which suggests that information about COVID-19 interventions has become more popular as the pandemic develops. Additionally, YouTube's search ranking algorithm seems to strongly take into account video polarization as videos that remain prominent in the search rankings have polarization scores 37% lower than videos that are removed from the top ten results. Topicality of video content may also play a role as medical treatment-related videos are the least promoted in the search results amongst the video subgroups despite having low video polarization. Engagement is lowest overall on medical intervention videos, which may be due to vaccine and treatment development as a topic being downgraded quickly from YouTube's search results. We recommend further research into YouTube's search result ranking model to better understand YouTube's role in the spread of news and information about coronavirus and other topics. Despite focusing on the COVID-19 pandemic, the methods for analyzing YouTube videos may be applied to other events or crises.

With the rise in popularity of social media, these platforms present a new opportunity to reach potential job candidates for employment opportunities. The current literature lacks sufficient research on methods and best practices to design and assess the efficacy of recruit and hire campaigns delivered on social media. We present a case study of a government e-recruiting effort discovered on Twitter. We collected almost 20 thousand tweets using the hashtag #FBIJobs, this included both Tweets and Retweets. Applications of descriptive statistics, topic modeling, sentiment analysis, and graph analytics identify where the campaign may miss potentially interested job candidates. We also find evidence of “popularity transfer” where co-mentions appear to increase the visibility of an accounts content in public feeds, without transferring the sentiment surrounding the more popular account. The research and findings were based on a publicly available e-recruiting campaign found online, without any inside knowledge or influence on campaign design or execution. Recommendations to better focus e-recruiting campaigns are provided.

We review the k-truss algorithm for community detection in networks. The k-truss is an efficient clustering algorithm that holds advantageous properties for many network applications. In this paper, we compare the k-truss performance against other, more well-known community detection algorithms. The k-truss is uniformly more computationally efficient than the Louvain and Clauset-Newman-Moore algorithms in terms of speed and memory with comparable modularity. Potential applications are discussed.

Twitter has become an important tool for communication and marketing. Topic model algorithms meant to characterize the discourse of online conversations and identify relevant audiences do not perform well for this task, despite their widespread usage. This paper proposes an iterative topic model, Gamma Filtration, and a social network-based method, Simmelian Filtration, to amplify tweet-topic probability signal and reduce noise. We demonstrate the method on a novel data set collected of European Racially and Ethnically Motivated Violent Extremist (REMVE) networks on Twitter. We find that Simmelian Filtering is most successful at reducing noise as measured by perplexity. This improves our ability to detect and monitor core conversations of a community that is disseminating propaganda to increase online extremism.

Customer satisfaction surveys, which have been the most common way of gauging customer feedback, involve high costs, require customer active participation, and typically involve low response rates. The tremendous growth of social media platforms such as Twitter provides businesses an opportunity to continuously gather and analyze customer feedback, with the goal of identifying and rectifying issues. This paper examines the alternative of replacing traditional customer satisfaction surveys with social media data. To evaluate this approach the following steps were taken, using customer feedback data extracted from Twitter: 1) Applying sentiment to each Tweet to compare the overall sentiment across different products and/or services. 2) Constructing a hashtag cooccurrence network to further optimize the customer feedback query process from Twitter. 3) Comparing customer feedback from survey responses with social media feedback, while considering content and added value. We find that social media provides advantages over traditional surveys.

This paper examines quantity and quality superposter value creation within Coursera Massive Open Online Courses (MOOC) forums using a social network analysis (SNA) approach. The value of quantity superposters (i.e. students who post significantly more often than the majority of students) and quality superposters (i.e. students who receive significantly more upvotes than the majority of students) is assessed using Stochastic Actor-Oriented Modeling (SAOM) and network centrality calculations. Overall, quantity and quality superposting was found to have a significant effect on tie formation within the discussion networks. In addition, quantity and quality superposters were found to have higher-than-average information brokerage capital within their networks.

ISIS and similar extremist communities are increasingly using forums in the darknet to connect with each other and spread news and propaganda. In this paper, we attempt to understand their network in an online forum by using descriptive statistics, an exponential random graph model (ERGM) and Topic Modeling. Our analysis shows how the cohesion between active members forms and grows over time and under certain thread topics. We find that the top attendants of the forum have high centrality measures and other attributes of influencers.

Bots are often identified on social media due to their behavior. How easily are they identified, however, when they are dormant and exhibit no measurable behavior at all, except for their silence? We identified "dormant bot networks" positioned to influence social media discourse surrounding the 2018 U.S. senate election. A dormant bot is a social media persona that does not post content yet has large follower and friend relationships with other users. These relationships may be used to manipulate online narratives and elevate or suppress certain discussions in the social media feed of users. Using a simple structure-based approach, we identify a large number of dormant bots created in 2017 that begin following the social media accounts of numerous US government politicians running for re-election in 2018. Findings from this research were used by the U.S. Government to suspend dormant bots prior to the elections to prevent any malign influence campaign. Application of this approach by social media providers may provide a novel method to reduce the risk of content manipulation for online platforms.

Basketball is an inherently social sport, which implies that social dynamics within a team may influence the team's performance on the court. As NBA players use social media, it may be possible to study the social structure of a team by examining the relationships that form within social media networks. This paper investigates the relationship between publicly available online social networks and quantitative performance data. It is hypothesized that network centrality measures for an NBA team's network will correlate with measurable performance metrics such as win percentage, points differential and assists per play. The hypothesis is tested using exponential random graph models (ERGM) and investigating correlation between network and performance variables. The results show that there are league-wide trends correlating certain network measures with game performance, and also quantifies the effects of various player attributes on network formation.

The United States is becoming increasingly politically divided. In addition to polarization between the two-major political parties, there is also divisiveness in intra-party dynamics. In this paper, we attempt to understand these intraparty divisions by using an exponential random graph model (ERGM) to compute a political cohesion metric to quantify the strength within the party at a given point in time. The analysis is applied to the 105th through 113th congressional sessions of the House of Representatives. We find that the Republican party not only generally exhibits stronger intra-party cohesion, but when voting patterns are broken out by topic, the party has a higher and more consistent cohesion factor compared to the Democratic Party.