1,720,982 research outputs found
That is a Known Lie: Detecting Previously Fact-Checked Claims
Full text linkThe recent proliferation of ”fake news” has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been fact-checked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again. As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked. Interestingly, despite the importance of the task, it has been largely ignored by the research community so far. Here, we aim to bridge this gap. In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work. We further create a specialized dataset, which we release to the research community. Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches
The Role of Context in Detecting Previously Fact-Checked Claims
Full text linkRecent years have seen the proliferation of disinformation and fake news
online. Traditional approaches to mitigate these issues is to use manual or
automatic fact-checking. Recently, another approach has emerged: checking
whether the input claim has previously been fact-checked, which can be done
automatically, and thus fast, while also offering credibility and
explainability, thanks to the human fact-checking and explanations in the
associated fact-checking article. Here, we focus on claims made in a political
debate and we study the impact of modeling the context of the claim: both on
the source side, i.e., in the debate, as well as on the target side, i.e., in
the fact-checking explanation document. We do this by modeling the local
context, the global context, as well as by means of co-reference resolution,
and multi-hop reasoning over the sentences of the document describing the
fact-checked claim. The experimental results show that each of these represents
a valuable information source, but that modeling the source-side context is
most important, and can yield 10+ points of absolute improvement over a
state-of-the-art model.Comment: Accepted as Findings of NAACL-2022, detecting previously fact-checked
claims, fact-checking, disinformation, fake news, social media, political
debate
Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
Full text linkSemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
Full text linkWe describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model
A Survey on Multimodal Disinformation Detection
Full text linkRecent years have witnessed the proliferation of offensive content online
such as fake news, propaganda, misinformation, and disinformation. While
initially this was mostly about textual content, over time images and videos
gained popularity, as they are much easier to consume, attract more attention,
and spread further than text. As a result, researchers started leveraging
different modalities and combinations thereof to tackle online multimodal
offensive content. In this study, we offer a survey on the state-of-the-art on
multimodal disinformation detection covering various combinations of
modalities: text, images, speech, video, social media network structure, and
temporal information. Moreover, while some studies focused on factuality,
others investigated how harmful the content is. While these two components in
the definition of disinformation (i) factuality, and (ii) harmfulness, are
equally important, they are typically studied in isolation. Thus, we argue for
the need to tackle disinformation detection by taking into account multiple
modalities as well as both factuality and harmfulness, in the same framework.
Finally, we discuss current challenges and future research directionsComment: Accepted at COLING-2022, disinformation, misinformation, factuality,
harmfulness, fake news, propaganda, multimodality, text, images, videos,
network structure, temporalit
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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