Predicting Influencer Virality on Twitter

The ability to successfully predict virality on Twitter holds great potential as a resource for Twitter influencers, enabling the development of more sophisticated strategies for audience engagement, audience monetization, and information sharing. To our knowledge, focusing exclusively on tweets posted by influencers is a novel context for studying Twitter virality. We find, among feature categories traditionally considered in the literature, that combining categories covering a range of information performs better than models only incorporating individual feature categories. Moreover, our general predictive model, encompassing a range of feature categories, achieves a prediction accuracy of 68% for influencer virality. We also investigate the role of influencer audiences in predicting virality, a topic we believe to be understudied in the literature. We suspect that incorporating audience information will allow us to better discriminate between virality classes, thus leading to better predictions. We pursue two different approaches, resulting in 10 different predictive models that leverage influencer audience information in addition to traditional feature categories. Both of our attempts to incorporate audience information plateau at an accuracy of approximately 61%, roughly a 7% decrease in performance compared to our general predictive model. We conclude that we are unable to find experimental evidence to support our claim that incorporating influencer audience information will improve virality predictions. Nonetheless, the performance of our general model holds promise for the deployment of a tool that allows influencers to reap the benefits of virality prediction. As stronger performance from the underlying model would make this tool more useful in practice to influencers, improving the predictive performance of our general model is a cornerstone of future work

Doing Big Things in a Small Way: A Social Media Analytics Approach to Information Diffusion During Crisis Events in Digital Influencer Networks

Digital influencers play an essential role in determining information diffusion during crisis events. This paper demonstrates that information diffusion (retweets) on the social media platform Twitter (now X) highly depends on digital influencers’ number of followers and influencers’ location within communication networks. We show (study 1) that there is significantly more information diffusion in regional (vs. national or international) crisis events when tweeted by micro-influencers (vs. meso- and macro-influencers). Further, study 2 demonstrates that this pattern holds when micro-influencers operate in a local location (are located local to the crisis). However, effects become attenuated when micro-influencers are situated in a global location (outside of the locality of the event). We term this effect ‘influencer network compression’ – the smaller in scope a crisis event geography (regional, national, or international) and influencer location (local or global) becomes, the more effective micro-influencers are at diffusing information. This shows that those who possess the most followers (meso- and macro-influencers) are less effective at attracting retweets than micro-influencers situated local to a crisis. As online information diffusion plays a critical role during public crisis events, this paper contributes to both practice and theory by exploring the role of digital influencers and their network geographies in different types of crisis events

#METOO: NETWORKED CELEBRITY ADVOCACY AS CAPITAL PERFORMANCE

Celebrity advocacy scholars have studied how social movements utilize celebrity appeal to attract media and public attention for decades. Some researchers have found that celebrity advocacy failed to achieve exceptional performance in the legacy media age. Moreover, only a very few top-class celebrities have successfully attracted legacy media attention regarding advocating social causes. This dissertation introduces the concept of networked celebrity advocacy to illustrate a new route on networked social media. Employing theories of capital and the framework of social network analysis, I test networked celebrity advocacy in the case of the #MeToo movement on Twitter. This dissertation analyzes the performance of top influencers in the Twitter #MeToo community from October 2017 to January 2018. The results provide evidence that networked celebrity advocacy functions on networked social media through the migration of celebrity capital and social capital, which encourages future research on underlying mechanisms of celebrity advocacy. Celebrities perform as brokers in online information traffic regarding social causes. This finding suggests that celebrities’ structural advantages in the online topic communities possibly affect their chance of attracting media attention for the public good, of which social activists can make use

FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe- art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud

Using communicative patterns to predict Twitter users' social capital, likability, and popularity gains with natural language processing

Social media constructs a computer-mediated public space where individuals' visibility and influence can be quantitatively measured by the number of likes, retweets, and followers they receive. These metrics serve as a reward system that not only reflects users' popularity and social capital but also influences the climate of public opinion and deliberative democracy by encouraging and discouraging certain types of communication. Through analyzing Twitter data collected from U.S. congressional politicians and ordinary U.S. Twitter users in seven/eight waves, this study explores how communicative patterns--dual-process styles and sentiment--predict users' social capital, likability, and popularity gains on Twitter as well as how political identity and intergroup communication moderate the relationships between these variables. It found that: (a) rational expressions increase social capital and popularity gains while emotional expressions increase likability gains; (b) positive expressions generate a curvilinear effect on social capital, likability, and popularity gains in the politician dataset; (c) compared with Democratic users, Republican users receive relatively more social capital, likability, and popularity gains from emotional and negative expressions than from rational and positive expressions; (d) rational expressions lead to relatively more likability and popularity gains than emotional expressions in a group-salient context; and (e) positive expressions in ingroup/outgroup conversations generate opposite effects in the politician and ordinary user datasets. In addition, this study develops and advances computational methods in detecting communicative patterns, political identities, and intergroup communication. By implementing Distributed Dictionary Representations, this study creates metrics to measure dual-process thinking styles and sentiment in text; by developing a two-step model with deep learning using an attention mechanism, this study creates an interpretable method to detect political partisanship and intergroup communication.Includes bibliographical references