Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.

Stance Detection: A Survey

Mental disorders are a major concern in societies all over the world, and in spite of the improved diagnosis rates of such disorders in recent years, many cases still go undetected. Nowadays, many people are increasingly utilising online social media platforms to share their feelings and moods. Despite the collective efforts in the community to develop models for identifying potential cases of mental disorders, not much work has been done to provide insights that could be used by a predictive system or a health practitioner in the elaboration of a diagnosis.

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Beyond Modelling: Understanding Mental Disorders in Online Social Media

Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions’ co-occurrence patterns, and online behavioural traits, including social engagement and posting trends. Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit. 

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

Triggered by Internet development, a large amount of information is published in online sources. However, it is a well-known fact that publications are inundated with inaccurate data. That is why fact-checking has become a significant topic in the last 5 years. It is widely accepted that factual data verification is a challenge even for the experts. This paper presents a domain-independent fact checking system. It can solve the fact verification problem entirely or at the individual stages. The proposed model combines various advanced methods of text data analysis, such as BERT and Infersent. The theoretical and empirical study of the system features is carried out. Based on FEVER and Fact Checking Challenge test-collections, experimental results demonstrate that our model can achieve the score on a par with state-of-the-art models designed by the specificity of particular datasets.

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Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification

The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches.

Internet users who suffer from variant types of mental disorders found in Social Media platforms and other online communities an easier way to reveal their minds. The textual content produced by such users proved to be handful in evaluating mental health and detecting related illnesses even in early stages. This paper overviews my recent doctoral work on eRisk 2019 shared task for early detection of Anorexia and early detection of Self-harm. These sub-tasks have many aspects to consider, including textual content, timing, and the similarities to other mental health prediction tasks. Thus, based on these different dimensions, we propose solutions based on Neural-Networks, Multi-task learning, domain adaptation and Markov Models.

Adapting Models for the Case of Early Risk Prediction on the Internet

In this paper, we describe our participation in CLEF eRisk workshop. eRisk 2019 is the third edition of this track3, which was first introduced in 2017. In the current edition, the organizers are targeting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks ofearly risk detection of Anorexia and self-harm. Our predictions are basedon Natural Language Processing using supervised machine learning with Support Vector Machines (SVM) and neural networks. SVM gave the best results among our five submitted models with latency-weighted F1of 0.58 andERDE5of 0.08 andERDE50of 0.04 for Anorexia detection task, while our more complicated neural network models did not show the desired performances.

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UDE at eRisk 2019: Early Risk Prediction on the Internet.

Social Media platforms have been a vital environment to share experiences and seek knowledge. People with various interests form online communities in which they can accumulate many experiences from many peers. Among these communities are the mental health-related ones that have been growing on Social Media in the last few years. However, users can show alarming behavioral signs at the stage of their mental illness that should be identified before it is too late. Hence, equipping social media platforms with the needed tools to monitor its users, identify risks, and intervene on time has been of great concern recently. In this paper, we target users who self disclose as being diagnosed with an eating disorder, namely Anorexia. We provide a dataset of manually labeled Reddit users’ posts, focused on the extraction of some potentially relevant topics for the study of eating disorders. E.g. diets, exercises, body image, etc. These topics can be utilized to find patterns in Anorexic users’ behaviors to distinguish them from users who are less likely to have Anorexia. They can also be used to interpret afflicted users’ attitudes. We support our labeling with baseline experiments to learn how to differentiate between these topics.

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Razan Masood

Papers

The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches.

Adapting Models for the Case of Early Risk Prediction on the Internet

UDE at eRisk 2019: Early Risk Prediction on the Internet.

Anorexia Topical Trends in Self-declared Reddit Users.

On Spatial Variation in the Detectability and Density of Social Media User Protest Supporters