Batch-based Active Learning: Application to Social Media Data for Crisis Management

Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media community. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL's performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power

Combating Misinformation on Social Media by Exploiting Post and User-level Information

Misinformation on social media has far-reaching negative impact on the public and society. Given the large number of real-time posts on social media, traditional manual-based methods of misinformation detection are not viable. Therefore, computational approaches (i.e., data-driven) have been proposed to combat online misinformation. Previous work on computational misinformation analysis has mainly focused on employing natural language processing (NLP) techniques to develop misinformation detection systems at the post level (e.g., using text and propagation network). However, it is also important to exploit information at the user level in social media, as users play a significant role (e.g., post, diffuse, refute, etc.) in spreading misinformation. The main aim of this thesis is to: (i) develop novel methods for analysing the behaviour of users who are likely to share or refute misinformation in social media; and (ii) predict and characterise unreliable stories with high popularity in social media. To this end, we first highlight the limitations in the evaluation protocol in popular rumour detection benchmarks on the post level and propose to evaluate such systems using chronological splits (i.e., considering temporal concept drift). On the user level, we introduce two novel tasks on (i) early detecting Twitter users that are likely to share misinformation before they actually do it; and (ii) identifying and characterising active citizens who refute misinformation in social media. Finally, we develop a new dataset to enable the study on predicting the future popularity (e.g. number of likes, replies, retweets) of false rumour on Weibo

ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION

Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models. Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time. We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used. Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)

VIRAL TOPIC PREDICTION AND DESCRIPTION IN MICROBLOG SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

FAKE NEWS DETECTION ON THE WEB: A DEEP LEARNING BASED APPROACH

The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. This work complements such research by proposing an approach that utilizes the amalgamation of Natural Language Processing (NLP), and Deep Learning techniques such as Long Short-Term Memory (LSTM). Moreover, in Information System’s paradigm, design science research methodology (DSRM) has become the major stream that focuses on building and evaluating an artifact to solve emerging problems. Hence, DSRM can accommodate deep learning-based models with the availability of adequate datasets. Two publicly available datasets that contain labeled news articles and tweets have been used to validate the proposed model’s effectiveness. This work presents two distinct experiments, and the results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets. Finally, the findings suggest that the sentiments, tagging, linguistics, syntactic, and text embeddings are the features that have the potential to foster fake news detection through training the proposed model on various dimensionality to learn the contextual meaning of the news content

Assessing Trust and Veracity of Data in Social Media

Social media highly impacts our knowledge and perception of the world. With the tremendous amount of data that is circulating in social media and initiated by a vast number of users from all over the world, extracting useful information from such data and assessing its veracity has become much more challenging. Data veracity refers to the trustworthiness and certainty of data. The challenges of handling textual data in social media have raised the need for efficient tools to extract, understand, and assess the veracity of information circulating in social media at a given time. In this thesis, we present three research problems to address major challenges of handling textual data in social media. First, overwhelming the user with huge volumes of short, noisy, and unstructured textual data complicates the task of understanding what topics are discussed by users in micro-blogging websites. Topic models were proposed to automatically learn a set of keywords that better describe each topic covered by a large corpus of text documents to enable fast and effective browsing and exploration of its contents. However, in order for the results of topic modeling algorithms to be useful, these results have to be interpretable. Applying topic models to social media data to get meaningful results is not a trivial task. In this thesis, we study the problem of improving interpretation of topic modeling of micro-posts in social media. We propose a new method that incorporates topic modeling, a lexical database, and the set of hashtags available in the corpus of micro-posts to produce a higher quality representation of each extracted topic. Extensive experiments on two real-life datasets collected from Twitter show that our method outperforms the state-of-the-art model in terms of perplexity, topics' coherence, and their quality. Second, the nature and flexibility of social media facilitate the process of posting unverified information, especially during the rapid diffusion of breaking news. Efficiently detecting and acting upon unverified breaking news rumors throughout social media is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this thesis, we study the problem of assessing the veracity of information contained in micro-posts regarding emerging stories and topics of breaking news. We propose a new approach that jointly learns word embeddings and trains a neural network model with two different objectives to automatically identify unverified micro-posts spreading in social media during breaking news. Extensive experiments on real-life datasets show that our proposed model outperforms the state-of-the-art classifier as well as other baseline classifiers in terms of precision, recall, and F1. Finally, the uncertainty and chaos associated with hot and sensitive breaking news and emergencies facilitate the explosive spread of high-engaging breaking news rumors that might be extremely damaging. In such a case, authorities have to prioritize the rumors verification process and act upon high-engaging breaking news rumors quickly to reduce their damaging consequences. However, this is an extremely challenging task. In this thesis, we study the problem of identifying rumors micro-posts that are most likely to become viral and achieve high engagement rates among recipients in social media during breaking news. We propose a multi-task neural network to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction. Extensive experiments on real-life datasets show that the performance of our joint learning model outperforms other baseline classifiers in terms of precision, recall, and F1 and is capable of identifying high-engaging breaking news rumors with high accuracy

Leveraging social relevance : using social networks to enhance literature access and microblog search

L'objectif principal d'un système de recherche d'information est de sélectionner les documents pertinents qui répondent au besoin en information exprimé par l'utilisateur à travers une requête. Depuis les années 1970-1980, divers modèles théoriques ont été proposés dans ce sens pour représenter les documents et les requêtes d'une part et les apparier d'autre part, indépendamment de tout utilisateur. Plus récemment, l'arrivée du Web 2.0 ou le Web social a remis en cause l'efficacité de ces modèles du fait qu'ils ignorent l'environnement dans lequel l'information se situe. En effet, l'utilisateur n'est plus un simple consommateur de l'information mais il participe également à sa production. Pour accélérer la production de l'information et améliorer la qualité de son travail, l'utilisateur échange de l'information avec son voisinage social dont il partage les mêmes centres d'intérêt. Il préfère généralement obtenir l'information d'un contact direct plutôt qu'à partir d'une source anonyme. Ainsi, l'utilisateur, influencé par son environnement socio-cultuel, donne autant d'importance à la proximité sociale de la ressource d'information autant qu'à la similarité des documents à sa requête. Dans le but de répondre à ces nouvelles attentes, la recherche d'information s'oriente vers l'implication de l'utilisateur et de sa composante sociale dans le processus de la recherche. Ainsi, le nouvel enjeu de la recherche d'information est de modéliser la pertinence compte tenu de la position sociale et de l'influence de sa communauté. Le second enjeu est d'apprendre à produire un ordre de pertinence qui traduise le mieux possible l'importance et l'autorité sociale. C'est dans ce cadre précis, que s'inscrit notre travail. Notre objectif est d'estimer une pertinence sociale en intégrant d'une part les caractéristiques sociales des ressources et d'autre part les mesures de pertinence basées sur les principes de la recherche d'information classique. Nous proposons dans cette thèse d'intégrer le réseau social d'information dans le processus de recherche d'information afin d'utiliser les relations sociales entre les acteurs sociaux comme une source d'évidence pour mesurer la pertinence d'un document en réponse à une requête. Deux modèles de recherche d'information sociale ont été proposés à des cadres applicatifs différents : la recherche d'information bibliographique et la recherche d'information dans les microblogs. Les importantes contributions de chaque modèle sont détaillées dans la suite. Un modèle social pour la recherche d'information bibliographique. Nous avons proposé un modèle générique de la recherche d'information sociale, déployé particulièrement pour l'accès aux ressources bibliographiques. Ce modèle représente les publications scientifiques au sein d'réseau social et évalue leur importance selon la position des auteurs dans le réseau. Comparativement aux approches précédentes, ce modèle intègre des nouvelles entités sociales représentées par les annotateurs et les annotations sociales. En plus des liens de coauteur, ce modèle exploite deux autres types de relations sociales : la citation et l'annotation sociale. Enfin, nous proposons de pondérer ces relations en tenant compte de la position des auteurs dans le réseau social et de leurs mutuelles collaborations. Un modèle social pour la recherche d'information dans les microblogs.} Nous avons proposé un modèle pour la recherche de tweets qui évalue la qualité des tweets selon deux contextes: le contexte social et le contexte temporel. Considérant cela, la qualité d'un tweet est estimé par l'importance sociale du blogueur correspondant. L'importance du blogueur est calculée par l'application de l'algorithme PageRank sur le réseau d'influence sociale. Dans ce même objectif, la qualité d'un tweet est évaluée selon sa date de publication. Les tweets soumis dans les périodes d'activité d'un terme de la requête sont alors caractérisés par une plus grande importance. Enfin, nous proposons d'intégrer l'importance sociale du blogueur et la magnitude temporelle avec les autres facteurs de pertinence en utilisant un modèle Bayésien.An information retrieval system aims at selecting relevant documents that meet user's information needs expressed with a textual query. For the years 1970-1980, various theoretical models have been proposed in this direction to represent, on the one hand, documents and queries and on the other hand to match information needs independently of the user. More recently, the arrival of Web 2.0, known also as the social Web, has questioned the effectiveness of these models since they ignore the environment in which the information is located. In fact, the user is no longer a simple consumer of information but also involved in its production. To accelerate the production of information and improve the quality of their work, users tend to exchange documents with their social neighborhood that shares the same interests. It is commonly preferred to obtain information from a direct contact rather than from an anonymous source. Thus, the user, under the influenced of his social environment, gives as much importance to the social prominence of the information as the textual similarity of documents at the query. In order to meet these new prospects, information retrieval is moving towards novel user centric approaches that take into account the social context within the retrieval process. Thus, the new challenge of an information retrieval system is to model the relevance with regards to the social position and the influence of individuals in their community. The second challenge is produce an accurate ranking of relevance that reflects as closely as possible the importance and the social authority of information producers. It is in this specific context that fits our work. Our goal is to estimate the social relevance of documents by integrating the social characteristics of resources as well as relevance metrics as defined in classical information retrieval field. We propose in this work to integrate the social information network in the retrieval process and exploit the social relations between social actors as a source of evidence to measure the relevance of a document in response to a query. Two social information retrieval models have been proposed in different application frameworks: literature access and microblog retrieval. The main contributions of each model are detailed in the following. A social information model for flexible literature access. We proposed a generic social information retrieval model for literature access. This model represents scientific papers within a social network and evaluates their importance according to the position of respective authors in the network. Compared to previous approaches, this model incorporates new social entities represented by annotators and social annotations (tags). In addition to co-authorships, this model includes two other types of social relationships: citation and social annotation. Finally, we propose to weight these relationships according to the position of authors in the social network and their mutual collaborations. A social model for information retrieval for microblog search. We proposed a microblog retrieval model that evaluates the quality of tweets in two contexts: the social context and temporal context. The quality of a tweet is estimated by the social importance of the corresponding blogger. In particular, blogger's importance is calculated by the applying PageRank algorithm on the network of social influence. With the same aim, the quality of a tweet is evaluated according to its date of publication. Tweets submitted in periods of activity of query terms are then characterized by a greater importance. Finally, we propose to integrate the social importance of blogger and the temporal magnitude tweets as well as other relevance factors using a Bayesian network model