Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

Video games as meaningful entertainment experiences

We conducted an experiment to examine individuals’ perceptions of enjoyable and meaningful video games and the game characteristics and dimensions of need satisfaction associated with enjoyment and appreciation. Participants (N = 512) were randomly assigned to 1 of 2 groups that asked them to recall a game that they found either particularly fun or particularly meaningful, and to then rate their perceptions of the game that they recalled. Enjoyment was high for both groups, though appreciation was higher in the meaningful- than fun-game condition. Further, enjoyment was most strongly associated with gameplay characteristics and satisfaction of needs related to competency and autonomy, whereas appreciation was most strongly associated with story characteristics and satisfaction of needs related to insight and relatedness

Comprehension Models of Audiovisual Discourse Processing

Comprehension is integral to enjoyment of media narratives, yet our understanding of how viewers create the situation models that underlie comprehension is limited.This study utilizes two models of comprehension that had previously been tested with factual texts/videos to predict viewers’ recall of entertainment media. Across five television/film clips, the landscape model explained at least 29% of the variance in recall. A dual coding version that assumed separate verbal and visual representations of the story significantly improved the model fit in four of the clips, accounting for an additional 15–29% of the variance. The dimensions of the event-indexingmodel (time, space, protagonist, causality, and intentionality) significantly moderated the relationship between the dual coding model and participant recall in all clips

MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction

Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues

Crisis of confidence : re-narrating the consumer-professional discourse

The professional-consumer relationship in professional services has undergone unprecedented change. Relationships which were traditionally dominated by respect for professional status are in flux as increasingly educated consumers challenge the professional establishment. This paper considers the nature of the professional service consumer and the implications for professional service encounters. Based on qualitative interviews we identify four patterns of consumer-professional interaction, compliant, collaborative, confirmatory, and consumerist, which reflect the nature of the discourse between consumer and professiona

PopRank: Ranking pages' impact and users' engagement on Facebook

Users online tend to acquire information adhering to their system of beliefs and to ignore dissenting information. Such dynamics might affect page popularity. In this paper we introduce an algorithm, that we call PopRank, to assess both the Impact of Facebook pages as well as users' Engagement on the basis of their mutual interactions. The ideas behind the PopRank are that i) high impact pages attract many users with a low engagement, which means that they receive comments from users that rarely comment, and ii) high engagement users interact with high impact pages, that is they mostly comment pages with a high popularity. The resulting ranking of pages can predict the number of comments a page will receive and the number of its posts. Pages impact turns out to be slightly dependent on pages' informative content (e.g., science vs conspiracy) but independent of users' polarization.Comment: 10 pages, 5 figure

Property and the Construction of the Information Economy: A Neo-Polanyian Ontology

This chapter considers the changing roles and forms of information property within the political economy of informational capitalism. I begin with an overview of the principal methods used in law and in media and communications studies, respectively, to study information property, considering both what each disciplinary cluster traditionally has emphasized and newer, hybrid directions. Next, I develop a three-part framework for analyzing information property as a set of emergent institutional formations that both work to produce and are themselves produced by other evolving political-economic arrangements. The framework considers patterns of change in existing legal institutions for intellectual property, the ongoing dematerialization and datafication of both traditional and new inputs to economic production, and the emerging logics of economic organization within which information resources (and property rights) are mobilized. Finally, I consider the implications of that framing for two very different contemporary information property projects, one relating to data flows within platform-based business models and the other to information commons