Online Social Networks and Media最新文献

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-the-art competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings.

Though a fair amount of research is being done to address disinformation in online social media, it has so far managed to stay ahead of the researchers’ learning curves forcing the publishers to rely on manual effort to a large extent. The root cause lies in the complex multi-contextual nature of the problem. The way a disinformation propagates on the social graph depends on multiple contexts i.e., content of the original news, credibility of the news source, poster of the message referring the news, message content, recipients of message with their social as well as psychological backgrounds, the role played by the available knowledge, and the temporal as well as the propagation pattern while the message becomes viral on the social graph. This article reviews each of these contexts to define the multi-contextual learning problem and summarizes the work done using each of them. Multi-contextual learning gets exacerbated by few other challenges. This article also reviews the approaches adopted so far to tackle each of these challenges along with an exhaustive review of the multi-contextual learning strategies adopted so far. The multi-contextuality aspect as well as the related challenges are horizontal in nature across the three primary verticals of disinformation i.e., fake news, rumor, and propaganda. Existing review articles primarily tackle one of these verticals in isolation with one or few of the above mentioned contexts. Also the related challenges have not seen any focused review so far. This article seeks to address these gaps by offering a comprehensive systemic view across this domain and concludes with a list of future research directions.

This paper presents an analysis of a temporal network that describes the social connections of a large-scale (∼30,000) sample of online social network users, inhabitants of a fixed city. We tested how the main network formation determinants—transitivity, preferential attachment, and social selection—contribute to network evolution. We obtained that tie appearing and tie removing events are governed by different combinations of mechanisms: whereas the structure of the network is responsible for the formation of new ties, nodal nonstructural characteristics “decide” whether a tie will continue to exist. Next, our findings show that only one network formation mechanism, gender selectivity, has a significant impact on both tie appearing and tie removing processes. What is interesting, the effect of gender selectivity is most notable for low-degree vertices. Besides this, our analysis revealed that opinion selectivity appears to be a noticeable (but not very important) factor only in the case of tie removing, whereas its contribution to tie appearing is elusive. Our findings suggest that nodes’ activity is a crucial factor of network evolution—the majority of tie removing events can be explained by the age-based activity mechanism. Finally, we report that transitivity and preferential attachment do govern network evolution. However, there are two important details: (i) their zone of influence is restricted primarily by tie appearing and (ii) the preferential attachment mechanism is replaced by the anti-preferential attachment rule if the number of common peers is greater than zero.

Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians’ negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.

Human fact-checkers currently represent a key component of any semi-automatic misinformation detection pipeline. While current state-of-the-art systems are mostly based on geometric deep-learning models, these architectures still need human-labeled data to be trained and updated — due to shifting topic distributions and adversarial attacks. Most research on automatic misinformation detection, however, neither considers time budget constraints on the number of pieces of news that can be manually fact-checked, nor tries to reduce the burden of fact-checking on – mostly pro bono – annotators and journalists. The first contribution of this work is a thorough analysis of active learning (AL) strategies applied to Graph Neural Networks (GNN) for misinformation detection. Then, based on this analysis, we propose Deep Error Sampling (DES) — a new deep active learning architecture that, when coupled with uncertainty sampling, performs equally or better than the most common AL strategies and the only existing active learning procedure specifically targeting fake news detection. Overall, our experimental results on two benchmark datasets show that all AL strategies outperform random sampling, allowing – on average – to achieve a 2% increase in AUC for the same percentage of third-party fact-checked news and to save up to 25% of labeling effort for a desired level of classification performance. As for DES, while it does not always clearly outperform other strategies, it still reduces variance in the performance between rounds, resulting in a more reliable method. To the best of our knowledge, we are the first to comprehensively study active learning in the context of misinformation detection and to show its potential to reduce the burden of third-party fact-checking without compromising classification performance.

Understanding how social media users interact with each other and spread information across multiple platforms is critical for developing effective methods for promoting truthful information and disrupting misinformation, as well as accurately simulating multi-platform information diffusion. This work explores five approaches for identifying relationships between users involved in cross-platform information spread. We use a combination of user attributes and URL posting behaviors to find users who appear to purposely spread the same information over multiple platforms or transfer information to new platforms. To evaluate the outlined approaches, we apply them to a dataset of over 24M social media posts from Twitter, Facebook, Reddit, and Instagram relating to the 2020 U.S. presidential election. We then characterize and validate our results using null model analysis and the component structure of the user networks returned by each approach. We subsequently examine the political bias, fact ratings, and performance of the content posted by the identified sets of users. We find that the different approaches yield largely distinct sets of users with different biases and content preferences.

In this paper, we apply the construal level theory to examine how temporal, social, and geographical distance affect the responses of social media users who have been confronted with the 2020 Vienna terror attack. We report on a long-term analysis that covers a time period of one year. The analysis is based on a data-set of more than 500,000 Twitter messages.

Our findings indicate that proximity to the event plays a significant role in how people cope with a terror attack. For example, we found that users with closer social bonds to people who have been directly affected by the attack, as well as users who have been geographically closer to the location of the attack, contributed more to the Twitter discourse than users with a larger social or geographical distance to the event. However, we also found that death anxiety was most intense in users located the furthest away from the attack, in different countries all around the world. Thus, a larger geographical distance to a terror attack seems to increase the level of death anxiety and the psychological effects induced by terror attacks are not restricted to people who are socially or geographically close to an attack. Among other things, we also found that religious references have been used in positive as well as negative responses. We used the Linguistic Inquiry and Word Count (LIWC) tool to identify psycholinguistic features in our data-set.

The development of data-driven Artificial Intelligence systems has seen successful application in diverse domains related to social platforms; however, many of these systems cannot explain the rationale behind their decisions. This is a major drawback, especially in critical domains such as those related to cybersecurity, of which malicious behavior on social platforms is a clear example. In light of this problem, in this paper we make several contributions: (i) a proposal of desiderata for the explanation of outputs generated by AI-based cybersecurity systems; (ii) a review of approaches in the literature on Explainable AI (XAI) under the lens of both our desiderata and further dimensions that are typically used for examining XAI approaches; (iii) the Hybrid Explainable and Interpretable Cybersecurity (HEIC) application framework that can serve as a roadmap for guiding R&D efforts towards XAI-based socio-technical systems; (iv) an example instantiation of the proposed framework in a news recommendation setting, where a portion of news articles are assumed to be fake news; and (v) exploration of various types of explanations that can help different kinds of users to identify real vs. fake news in social platform settings.

This article explores how political actors use the emotions of fear and anger in what we call fear-anger contests. Our theory distinguishes between governmental and populist actors and posits that, in a contest for media attention and the hearts and minds of citizens, populists pursue a politics of anger whereas governmental actors pursue a politics of fear. To evaluate the theory, we examine two episodes of contentious politics: the 2016 Brexit referendum and the election of Donald Trump in the same year. We rely on automated sentiment analysis, using machine learning and emotion dictionaries to examine a dataset of social media posts on Twitter. In the case of Brexit, we find a fear-anger contest between Remain (“Project Fear”) and Leave (“Project Anger”). In the case of the 2016 US presidential election, we find a negativity contest where both parties reinforce each other's negative emotions.