Topology comparison of Twitter diffusion networks effectively reveals misleading information

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.Comment: A revised new version is available on Scientific Report

Predicting the security threats of internet rumors and spread of false information based on sociological principle

With the fast-growing IoT, regular connectivity through a range of heterogeneous intelligent devices across the Social Online Networks (SON) is feasible and effective to analyze sociological principles. Therefore, Increased user contributions, including web posts, videos and reviews slowly impact the lives of people in the recent past, which triggers volatile knowledge dissemination and undermine protection through gossip dissemination, disinformation, and offensive online debate. Based on the early diffusion status, the goal of this research is to forecast the popularity of online content reliably in the future. Though conventional prediction models are focused primarily on the discovery or integration of a network functionality into a changing time mechanism has been considered as unresolved issues and it has been resolved using Predicting The Security Threats of Internet Rumors (PSTIR) and Spread of False Information Based On Sociological (SFIBS) model with sociology concept. In this paper, the proportion of trustworthy Facebook fans who post regularly in early and future popularity has been analyzed linearly using PSTIR and SFIBS methods. Facebook statistics remind us that mainstream fatigue is an important prediction principle and The mainstream fatigue principle, Besides, it shows the effectiveness of the PSTIR and SFIBS based on experimental stud

Toxicity in Evolving Twitter Topics - Employing a novel Dynamic Topic volution Model (DyTEM) onTwitter data

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis thesis presents an extensive investigation into the evolution of topics and their association with speech toxicity on Twitter, based on a large corpus of tweets, providing crucial insights for monitoring online discourse and potentially informing interventions to combat toxic behavior in digital communities. A Dynamic Topic Evolution Model (DyTEM) is introduced, constructed by combining static Topic Modelling techniques and sentence embeddings through the state-of-the-art sentence transformer, sBERT. The DyTEM, tested and validated on a substantial sample of tweets, is represented as a directed graph, encapsulating the inherent dynamism of Twitter discussions. For validating the consistency of DyTEM and providing guidance for hyperparameter selection, a novel, hashtag-based validation method is proposed. The analysis identifies and scrutinizes five distinct Topic Transition Types: Topic Stagnation, Topic Merge, Topic Split, Topic Disappearance, and Topic Emergence. A speech toxicity classification model is employed to delve into the toxicity dynamics within topic evolution. A standout finding of this study is the positive correlation between topic popularity and its toxicity, implying that trending or viral topics tend to contain more inflammatory speech. This insight, along with the methodologies introduced in this study, contributes significantly to the broader understanding of digital discourse dynamics and could guide future strategies aimed at fostering healthier and more constructive online spaces

Tackling threats to informed decision-making in democratic societies: Promoting epistemic security in a technologically-advanced world

Access to reliable information is crucial to the ability of a democratic society to coordinate effective collective action when responding to a crisis, like a global pandemic, or complex challenge like climate change. Through a series of workshops we developed and analysed a set of hypothetical yet plausible crisis scenarios to explore how technologically exacerbated external threats and internal vulnerabilities to a society’s epistemic security – its ability to reliably avert threats to the processes by which reliable information is produced, distributed, and assessed within the society – can be mitigated in order to facilitate timely decision-making and collective action in democratic societies. Overall we observed that preserving a democratic society’s epistemic security is a complex effort that sits at the interface of many knowledge domains, theoretical perspectives, value systems, and institutional responsibilities, and we developed a series of recommendations to highlight areas where additional research and resources will likely have a significant impact on improving epistemic security in democratic societie

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

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Cyber Security Politics

This book examines new and challenging political aspects of cyber security and presents it as an issue defined by socio-technological uncertainty and political fragmentation. Structured along two broad themes and providing empirical examples for how socio-technical changes and political responses interact, the first part of the book looks at the current use of cyber space in conflictual settings, while the second focuses on political responses by state and non-state actors in an environment defined by uncertainties. Within this, it highlights four key debates that encapsulate the complexities and paradoxes of cyber security politics from a Western perspective – how much political influence states can achieve via cyber operations and what context factors condition the (limited) strategic utility of such operations; the role of emerging digital technologies and how the dynamics of the tech innovation process reinforce the fragmentation of the governance space; how states attempt to uphold stability in cyberspace and, more generally, in their strategic relations; and how the shared responsibility of state, economy, and society for cyber security continues to be re-negotiated in an increasingly trans-sectoral and transnational governance space. This book will be of much interest to students of cyber security, global governance, technology studies, and international relations