122 research outputs found
Stance detection on social media: State of the art and trends
Stance detection on social media is an emerging opinion mining paradigm for
various social and political applications in which sentiment analysis may be
sub-optimal. There has been a growing research interest for developing
effective methods for stance detection methods varying among multiple
communities including natural language processing, web science, and social
computing. This paper surveys the work on stance detection within those
communities and situates its usage within current opinion mining techniques in
social media. It presents an exhaustive review of stance detection techniques
on social media, including the task definition, different types of targets in
stance detection, features set used, and various machine learning approaches
applied. The survey reports state-of-the-art results on the existing benchmark
datasets on stance detection, and discusses the most effective approaches. In
addition, this study explores the emerging trends and different applications of
stance detection on social media. The study concludes by discussing the gaps in
the current existing research and highlights the possible future directions for
stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this
paper. Please withdraw this article before we finish the new versio
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
Rumor Detection with Diverse Counterfactual Evidence
The growth in social media has exacerbated the threat of fake news to
individuals and communities. This draws increasing attention to developing
efficient and timely rumor detection methods. The prevailing approaches resort
to graph neural networks (GNNs) to exploit the post-propagation patterns of the
rumor-spreading process. However, these methods lack inherent interpretation of
rumor detection due to the black-box nature of GNNs. Moreover, these methods
suffer from less robust results as they employ all the propagation patterns for
rumor detection. In this paper, we address the above issues with the proposed
Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our
intuition is to exploit the diverse counterfactual evidence of an event graph
to serve as multi-view interpretations, which are further aggregated for robust
rumor detection results. Specifically, our method first designs a subgraph
generation strategy to efficiently generate different subgraphs of the event
graph. We constrain the removal of these subgraphs to cause the change in rumor
detection results. Thus, these subgraphs naturally serve as counterfactual
evidence for rumor detection. To achieve multi-view interpretation, we design a
diversity loss inspired by Determinantal Point Processes (DPP) to encourage
diversity among the counterfactual evidence. A GNN-based rumor detection model
further aggregates the diverse counterfactual evidence discovered by the
proposed DCE-RD to achieve interpretable and robust rumor detection results.
Extensive experiments on two real-world datasets show the superior performance
of our method. Our code is available at https://github.com/Vicinity111/DCE-RD
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