Misinformation Detection in Social Media

abstract: The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity. The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Essays on Social Media and Digital Marketing

Digital technology is rapidly reshaping the way how brands interact with consumers. More and more marketers are shifting their focus from traditional marketing channels (e.g., TV) to digital channels (e.g., social media platforms). Effective targeting is key to successful social media and digital marketing campaigns. This dissertation seeks to shed light on who and how to target on social media platforms. The first chapter aims to provide insights on how to target customers who are connected to each other on social media platforms. We investigate how the network embeddedness (i.e., number of common followees, common followers, and common mutual followers) between two users impacts information diffusion from one (sender) to another (receiver). By analyzing the sharing of sponsored ads on Digg and brand-authored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. A receiver is more likely to share content from a sender if they share more common followees. A receiver is also more likely to share content if she shares more common followers and common mutual followers with the sender. However, this effect is moderated by the novelty of information. The second chapter strives to understand what affects paid endorsers’ participation and effectiveness in social advertising campaigns. We conduct a field experiment with an invitation design in which we manipulate both incentives and a soft eligibility requirement to participate in the campaign. There are three main findings from our analysis. (1) Payments higher than the average reward a potential endorser received in the past (gains) do not increase participation, whereas lower payments (losses) decrease participation. Neither gains nor losses compared to past reward affect performance. (2) Potential endorsers who are more likely to participate tend to be less effective. (3) Which characteristics are associated with effectiveness depends on whether success is measured in likes, comments, or retweets. For marketing managers, our findings provide insights on how to target customers in a directed network at a micro level and how to improve social advertising campaigns by better targeting and incenting potential endorsers

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot