Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted

Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations

Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users’ replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations

Stance Prediction for Russian: Data and Analysis

Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language

Humanitarianism 2.0

It is difficult to overstate the importance of trust in a world where global networks facilitate the constant flow of contradictory information. The search for verifiable leads and trusted sources is a central facet of daily communication and is becoming more so as our connections with one another become more decontextualised, geographically distant and, increasingly entirely virtual. The swell of internet connection rates across the world has meant an explosion of interaction and allowed new opportunities for global collective action. Whilst countless words have been written exploring the dangers of this global network and the threats that “new media” represents to social structures and moral fabrics, this collection seeks to explore the role that new social technologies are having in the world of humanitarianism and conflict response

Context-Aware Message-Level Rumour Detection with Weak Supervision

Social media has become the main source of all sorts of information beyond a communication medium. Its intrinsic nature can allow a continuous and massive flow of misinformation to make a severe impact worldwide. In particular, rumours emerge unexpectedly and spread quickly. It is challenging to track down their origins and stop their propagation. One of the most ideal solutions to this is to identify rumour-mongering messages as early as possible, which is commonly referred to as "Early Rumour Detection (ERD)". This dissertation focuses on researching ERD on social media by exploiting weak supervision and contextual information. Weak supervision is a branch of ML where noisy and less precise sources (e.g. data patterns) are leveraged to learn limited high-quality labelled data (Ratner et al., 2017). This is intended to reduce the cost and increase the efficiency of the hand-labelling of large-scale data. This thesis aims to study whether identifying rumours before they go viral is possible and develop an architecture for ERD at individual post level. To this end, it first explores major bottlenecks of current ERD. It also uncovers a research gap between system design and its applications in the real world, which have received less attention from the research community of ERD. One bottleneck is limited labelled data. Weakly supervised methods to augment limited labelled training data for ERD are introduced. The other bottleneck is enormous amounts of noisy data. A framework unifying burst detection based on temporal signals and burst summarisation is investigated to identify potential rumours (i.e. input to rumour detection models) by filtering out uninformative messages. Finally, a novel method which jointly learns rumour sources and their contexts (i.e. conversational threads) for ERD is proposed. An extensive evaluation setting for ERD systems is also introduced

Rumors and Rumor Corrections on Twitter : Studying Message Characteristics and Opinion Leadership

As rumors often ripple across the cyberspace, posting rumor corrections on social media can bring about social good by spreading the truth. However, rumors and rumor corrections are not easily distinguishable from one another. Therefore, this paper investigates how three message characteristics, namely, the use of emotions, clarity and credible source attribution, can predict message veracity on social media. Message veracity denotes whether a message is a rumor or a rumor correction. In addition, the paper further examines the extent to which opinion leadership moderates the relation between message characteristics and message veracity. Set against the context of the death hoax of Singapore’s first Prime Minister Lee Kuan Yew in March 2015, data for this paper came from Twitter. Analysis involved binary logistic regression. All the three message characteristics predicted veracity. Rumor corrections were characterized by lower use of emotions, higher clarity, and higher credible source attribution compared with rumors. Furthermore, opinion leadership moderated the relation between the use of emotions and message veracity as well as that between credible source attribution and message veracity

Emotional and mental nuances and technological approaches: Optimising Fact-Check dissemination through cognitive reinforcement technique †

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and (Formula presented.) (formerly Twitter).FCT -Fundação para a Ciência e a Tecnologia(2022.06822

Rumour stance and veracity classification in social media conversations

Social media platforms are popular as sources of news, often delivering updates faster than traditional news outlets. The absence of verification of the posted information leads to wide proliferation of misinformation. The effects of propagation of such false information can have far-reaching consequences on society. Traditional manual verification by fact-checking professionals is not scalable to the amount of misinformation being spread. Therefore there is a need for an automated verification tool that would assist the process of rumour resolution. In this thesis we address the problem of rumour verification in social media conversations from a machine learning perspective. Rumours that attract a lot of scepticism in the form of questions and denials among the responses are more likely to be proven false later (Zhao et al., 2015). Thus we explore how crowd wisdom in the form of the stance of responses towards a rumour can contribute to an automated rumour verification system. We study the ways of determining the stance of each response in a conversation automatically. We focus on the importance of incorporating conversation structure into stance classification models and also identifying characteristics of supporting, denying, questioning and commenting posts. We follow by proposing several models for rumour veracity classification that incorporate different feature sets, including the stance of the responses, attempting to find the set that would lead to the most accurate models across several datasets. We view the rumour resolution process as a sequence of tasks: rumour detection, tracking, stance classification and, finally, rumour verification. We then study relations between the tasks in the rumour verification pipeline through a joint learning approach, showing its benefits comparing to single-task learning. Finally, we address the issue of transparency of model decisions by incorporating uncertainty estimation methods into rumour verification models. We then conclude and point directions for future research

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work