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

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods

Detection of Offensive YouTube Comments, a Performance Comparison of Deep Learning Approaches

Social media data is open, free and available in massive quantities. However, there is a significant limitation in making sense of this data because of its high volume, variety, uncertain veracity, velocity, value and variability. This work provides a comprehensive framework of text processing and analysis performed on YouTube comments having offensive and non-offensive contents. YouTube is a platform where every age group of people logs in and finds the type of content that most appeals to them. Apart from this, a massive increase in the use of offensive language has been apparent. As there are massive volume of new comments, each comment cannot be removed manually or it will be bad for business for youtubers if they make their comment section unavailable as they will not be able to get any feedback of any kind

WELLNESS PROFILING ON SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

Image Understanding by Socializing the Semantic Gap

Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community

Weakly-supervised Learning Approaches for Event Knowledge Acquisition and Event Detection

Capabilities of detecting events and recognizing temporal, subevent, or causality relations among events can facilitate many applications in natural language understanding. However, supervised learning approaches that previous research mainly uses have two problems. First, due to the limited size of annotated data, supervised systems cannot sufficiently capture diverse contexts to distill universal event knowledge. Second, under certain application circumstances such as event recognition during emergent natural disasters, it is infeasible to spend days or weeks to annotate enough data to train a system. My research aims to use weakly-supervised learning to address these problems and to achieve automatic event knowledge acquisition and event recognition. In this dissertation, I first introduce three weakly-supervised learning approaches that have been shown effective in acquiring event relational knowledge. Firstly, I explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts, and these rich contexts can be used to train a contextual temporal relation classifier to further recognize new temporal relation knowledge. Secondly, inspired by the double temporality characteristic of narrative texts, I propose a weakly supervised approach that identifies 287k narrative paragraphs using narratology principles and then extract rich temporal event knowledge from identified narratives. Lastly, I develop a subevent knowledge acquisition approach by exploiting two observations that 1) subevents are temporally contained by the parent event and 2) the definitions of the parent event can be used to guide the identification of subevents. I collect rich weak supervision to train a contextual BERT classifier and apply the classifier to identify new subevent knowledge. Recognizing texts that describe specific categories of events is also challenging due to language ambiguity and diverse descriptions of events. So I also propose a novel method to rapidly build a fine-grained event recognition system on social media texts for disaster management. My method creates high-quality weak supervision based on clustering-assisted word sense disambiguation and enriches tweet message representations using preceding context tweets and reply tweets in building event recognition classifiers

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.Comment: 52 pages, 6 figure

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality

The increasing popularity of social media has encouraged health consumers to share, explore, and validate health and wellness information on social networks, which provide a rich repository of Patient Generated Wellness Data (PGWD). While data-driven healthcare has attracted a lot of attention from academia and industry for improving care delivery through personalized healthcare, limited research has been done on harvesting and utilizing PGWD available on social networks. Recently, representation learning has been widely used in many applications to learn low-dimensional embedding of users. However, existing approaches for representation learning are not directly applicable to PGWD due to its domain nature as characterized by longitudinality, incompleteness, and sparsity of observed data as well as heterogeneity of the patient population. To tackle these problems, we propose an approach which directly learns the embedding from longitudinal data of users, instead of vector-based representation. In particular, we simultaneously learn a low-dimensional latent space as well as the temporal evolution of users in the wellness space. The proposed method takes into account two types of wellness prior knowledge: (1) temporal progression of wellness attributes; and (2) heterogeneity of wellness attributes in the patient population. Our approach scales well to large datasets using parallel stochastic gradient descent. We conduct extensive experiments to evaluate our framework at tackling three major tasks in wellness domain: attribute prediction, success prediction, and community detection. Experimental results on two real-world datasets demonstrate the ability of our approach in learning effective user representations

Towards Evaluating Veracity of Textual Statements on the Web

The quality of digital information on the web has been disquieting due to the absence of careful checking. Consequently, a large volume of false textual information is being produced and disseminated with misstatements of facts. The potential negative influence on the public, especially in time-sensitive emergencies, is a growing concern. This concern has motivated this thesis to deal with the problem of veracity evaluation. In this thesis, we set out to develop machine learning models for the veracity evaluation of textual claims based on stance and user engagements. Such evaluation is achieved from three aspects: news stance detection engaged user replies in social media and the engagement dynamics. First of all, we study stance detection in the context of online news articles where a claim is predicted to be true if it is supported by the evidential articles. We propose to manifest a hierarchical structure among stance classes: the high-level aims at identifying relatedness, while the low-level aims at classifying, those identified as related, into the other three classes, i.e., agree, disagree, and discuss. This model disentangles the semantic difference of related/unrelated and the other three stances and helps address the class imbalance problem. Beyond news articles, user replies on social media platforms also contain stances and can infer claim veracity. Claims and user replies in social media are usually short and can be ambiguous; to deal with semantic ambiguity, we design a deep latent variable model with a latent distribution to allow multimodal semantic distribution. Also, marginalizing the latent distribution enables the model to be more robust in relatively smalls-sized datasets. Thirdly, we extend the above content-based models by tracking the dynamics of user engagement in misinformation propagation. To capture these dynamics, we formulate user engagements as a dynamic graph and extract its temporal evolution patterns and geometric features based on an attention-modified Temporal Point Process. This allows to forecast the cumulative number of engaged users and can be useful in assessing the threat level of an individual piece of misinformation. The ability to evaluate veracity and forecast the scale growth of engagement networks serves to practically assist the minimization of online false information’s negative impacts