Cognitive satellite communications and representation learning for streaming and complex graphs.

This dissertation includes two topics. The first topic studies a promising dynamic spectrum access algorithm (DSA) that improves the throughput of satellite communication (SATCOM) under the uncertainty. The other topic investigates distributed representation learning for streaming and complex networks. DSA allows a secondary user to access the spectrum that are not occupied by primary users. However, uncertainty in SATCOM causes more spectrum sensing errors. In this dissertation, the uncertainty has been addressed by formulating a DSA decision-making process as a Partially Observable Markov Decision Process (POMDP) model to optimally determine which channels to sense and access. Large-scale networks have attracted many attentions to discover the hidden information from big data. Particularly, representation learning embeds the network into a lower vector space while maximally preserving the similarity among nodes. I propose a real-time distributed graph embedding algorithm (RTDGE) which is capable of distributively embedding the streaming graph by combining a novel edge partition approach and an incremental negative sample approach. Furthermore, a platform is prototyped based on Kafka and Storm. Real-time Twitter network data can be retrieved, partitioned and processed for state-of-art tasks. For knowledge graphs, existing works cannot capture the complex connection patterns and never consider the impacts from complicated relations, due to the unquantifiable relationships. A novel embedding algorithm is proposed to hierarchically measure the structural similarity and the impacts from relations by constructing a multi-layer graph. Then, an advanced representation learning model is designed based on an entity\u27s context generated by random walks on the multi-layer content graph

Online allies and tricky freelancers: understanding the differences in the role of social media in the campaigns for the Scottish Independence Referendum

Using the 2014 Scottish independence referendum as a case study, this article asks first, to what extent is the use of digital communications technologies, in particular social media, associated with fundamental changes to campaign organizations, specifically to the command and control model? Second, under what conditions are challenges to the model more likely to emerge? Using mixed methods, our analysis of the case demonstrates that radical organizational or strategic change is not inevitable, nor is there a one-size-fits-all approach. Technologies are not ‘just tools’ that any campaign with enough resources will adopt in similar ways. Instead, depending on a number of interdependent factors (i.e. context, resources, strategy, organizational structure and culture), some campaigns – like Better Together – selectively adopt digital tools that fit with the command and control model; in other cases – like Yes Scotland – the application of digital communications technologies and the dynamics created by linking to other (digital-enabled) grassroots organizations can have transformative effects

The Shapes of Cultures: A Case Study of Social Network Sites/Services Design in the U.S. and China

With growing popularity of the use of social network sites/services (SNSs) throughout the world, the global dominance of SNSs designed in the western industrialized countries, especially in the United Sates, seems to have become an inevitable trend. As internationalization has become a common practice in designing SNSs in the United States, is localization still a viable practice? Does culture still matter in designing SNSs? This dissertation aims to answer these questions by comparing the user interface (UI) designs of a U.S.-based SNS, Twitter, and a China-based SNS, Sina Weibo, both of which have assumed an identity of a “microblogging” service, a sub category of SNSs. This study employs the theoretical lens of the theory of technical identity, user-centered website cultural usability studies, and communication and media studies. By comparing the UI designs, or the “form,” of the two microblogging sites/services, I illustrate how the social functions of a technological object as embedded and expressed in the interface designs are preserved or changed as the technological object that has developed a relatively stable identity (as a microblogging site/service) in one culture is transferred between the “home” culture and another. The analysis in this study focuses on design elements relevant to users as members of networks, members of audience, and publishers/broadcasters. The results suggest that the designs carry disparate biases towards modes of communication and social affordances, which indicate a shift of the identity of microblogging service/site across cultures

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a typology of the Web's false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web

Predators and Principles: Think Tank Influence, Media Visibility, and Political Partisanship

By some measures, the major U.S. political parties have become more extreme in their political positions in recent decades, and scholars have raised concerns about whether the policy expertise provided by today’s think tanks has become similarly partisan and polarized. Furthermore, there is a perception that certain overtly partisan and highly visible think tanks wield considerable and growing influence over the policy platforms of the major U.S. parties, using their media presence to shape public and policymaker views of particular issues. Using publicly accessible tax, media, and congressional data, my proposed study assesses the extent to which media visibility and political partisanship explain the degree of influence that modern think tanks have on policy outcomes. First, I identify which think tanks are the most influential, as measured by interactions with policymakers—namely, requests to testify at congressional committees (Abelson, 2002; Rich & Weaver, 2000). I then use multiple regression analyses to assess to what extent these measures of influence are associated with think-tank media exposure (as measured by mentions in major news sources and social media metrics) and political partisanship, adapting the methodologies of Rich and Weaver (2000) to measure the former and Groseclose and Milyo (2005) to measure the latter

Scholarship in abundance: Influence, engagement, and attention in scholarly networks

In an era of knowledge abundance, scholars have the capacity to distribute and share ideas and artifacts via digital networks, yet networked scholarly engagement often remains unrecognized within institutional spheres of influence. The purpose of this dissertation study is to explore the meanings constructed and enacted within the networked practices of 13 scholars actively engaged in both institutional and networked participatory scholarship. Using ethnographic methods including participant observation, interviews, and document analysis, the study investigates networks as sites of scholarship, with the intent of furthering institutional academia’s understanding of networked practices. The three papers that make up the dissertation each articulate a specific thread of intersection between institutional and networked scholarship: the first focuses on what counts as academic influence within networked circles, the second on networks’ terms of value and reward, and the third on the relationships between attention, care, and vulnerability in scholarly networks. Together, the papers conclude that networked scholarly practices of engagement align broadly with those of academia, yet enable and demand scholars’ individual cultivation of influence, visibility, and audiences. Thus networked scholarship rewards connection, collaboration, and curation between individuals rather than roles or institutions, fostering cross-disciplinary and public engagement and a bridging of the personal/professional divide. The study contributes to knowledge by situating networked scholarly practices within the scholarly tradition, while articulating the terms on which knowledge abundance and networked practices open up new spheres of opportunity and vulnerability for scholars

A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented