Information Reliability on the Social Web - Models and Applications in Intelligent User Interfaces

The Social Web is undergoing continued evolution, changing the paradigm of information production, processing and sharing. Information sources have shifted from institutions to individual users, vastly increasing the amount of information available online. To overcome the information overload problem, modern filtering algorithms have enabled people to find relevant information in efficient ways. However, noisy, false and otherwise useless information remains a problem. We believe that the concept of information reliability needs to be considered along with information relevance to adapt filtering algorithms to today's Social Web. This approach helps to improve information search and discovery and can also improve user experience by communicating aspects of information reliability.This thesis first shows the results of a cross-disciplinary study into perceived reliability by reporting on a novel user experiment. This is followed by a discussion of modeling, validating, and communicating information reliability, including its various definitions across disciplines. A selection of important reliability attributes such as source credibility, competence, influence and timeliness are examined through different case studies. Results show that perceived reliability of information can vary greatly across contexts. Finally, recent studies on visual analytics, including algorithm explanations and interactive interfaces are discussed with respect to their impact on the perception of information reliability in a range of application domains

An Experimental Study of Structural Diversity in Social Networks

Several recent studies of online social networking platforms have found that adoption rates and engagement levels are positively correlated with structural diversity, the degree of heterogeneity among an individual's contacts as measured by network ties. One common theory for this observation is that structural diversity increases utility, in part because there is value to interacting with people from different network components on the same platform. While compelling, evidence for this causal theory comes from observational studies, making it difficult to rule out non-causal explanations. We investigate the role of structural diversity on retention by conducting a large-scale randomized controlled study on the Twitter platform. We first show that structural diversity correlates with user retention on Twitter, corroborating results from past observational studies. We then exogenously vary structural diversity by altering the set of network recommendations new users see when joining the platform; we confirm that this design induces the desired changes to network topology. We find, however, that low, medium, and high structural diversity treatment groups in our experiment have comparable retention rates. Thus, at least in this case, the observed correlation between structural diversity and retention does not appear to result from a causal relationship, challenging theories based on past observational studies.Comment: To appear in the Proceedings of International AAAI Conference on Web and Social Media (ICWSM 2020

Living analytics methods for the social web

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Protecting attributes and contents in online social networks

With the extreme popularity of online social networks, security and privacy issues become critical. In particular, it is important to protect user privacy without preventing them from normal socialization. User privacy in the context of data publishing and structural re-identification attacks has been well studied. However, protection of attributes and data content was mostly neglected in the research community. While social network data is rarely published, billions of messages are shared in various social networks on a daily basis. Therefore, it is more important to protect attributes and textual content in social networks. We first study the vulnerabilities of user attributes and contents, in particular, the identifiability of the users when the adversary learns a small piece of information about the target. We have presented two attribute-reidentification attacks that exploit information retrieval and web search techniques. We have shown that large portions of users with online presence are very identifiable, even with a small piece of seed information, and the seed information could be inaccurate. To protect user attributes and content, we adopt the social circle model derived from the concepts of "privacy as user perception" and "information boundary". Users will have different social circles, and share different information in different circles. We introduce a social circle discovery approach using multi-view clustering. We present our observations on the key features of social circles, including friendship links, content similarity and social interactions. We treat each feature as one view, and propose a one-side co-trained spectral clustering technique, which is tailored for the sparse nature of our data. We also propose two evaluation measurements. One is based on the quantitative measure of similarity ratio, while the other employs human evaluators to examine pairs of users, who are selected by the max-risk active evaluation approach. We evaluate our approach on ego networks of twitter users, and present our clustering results. We also compare our proposed clustering technique with single-view clustering and original co-trained spectral clustering techniques. Our results show that multi-view clustering is more accurate for social circle detection; and our proposed approach gains significantly higher similarity ratio than the original multi-view clustering approach. In addition, we build a proof-of-concept implementation of automatic circle detection and recommendation methods. For a user, the system will return its circle detection result from our proposed multi-view clustering technique, and the key words for each circle are also presented. Users can also enter a message they want to post, and the system will suggest which circle to disseminate the message

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

The Influence of Opinion Leaders towards the Purchase Intentions of Consumers in the Virtual Communities of Consumption

Tohoku University大滝精一課