163 research outputs found
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
Mining Social Media for Newsgathering: A Review
Social media is becoming an increasingly important data source for learning
about breaking news and for following the latest developments of ongoing news.
This is in part possible thanks to the existence of mobile devices, which
allows anyone with access to the Internet to post updates from anywhere,
leading in turn to a growing presence of citizen journalism. Consequently,
social media has become a go-to resource for journalists during the process of
newsgathering. Use of social media for newsgathering is however challenging,
and suitable tools are needed in order to facilitate access to useful
information for reporting. In this paper, we provide an overview of research in
data mining and natural language processing for mining social media for
newsgathering. We discuss five different areas that researchers have worked on
to mitigate the challenges inherent to social media newsgathering: news
discovery, curation of news, validation and verification of content,
newsgathering dashboards, and other tasks. We outline the progress made so far
in the field, summarise the current challenges as well as discuss future
directions in the use of computational journalism to assist with social media
newsgathering. This review is relevant to computer scientists researching news
in social media as well as for interdisciplinary researchers interested in the
intersection of computer science and journalism.Comment: Accepted for publication in Online Social Networks and Medi
LORE: a model for the detection of fine-grained locative references in tweets
[EN] Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], and the European Union's Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. We also thank Universidad de Granada for their financial support to the first author through the Becas de Iniciacion para estudiantes de Master 2018 del Plan Propio de la UGR.Fernández-Martínez, NJ.; Periñán-Pascual, C. (2021). LORE: a model for the detection of fine-grained locative references in tweets. Onomázein. (52):195-225. https://doi.org/10.7764/onomazein.52.111952255
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
A new era of Information Warfare has arrived. Various actors, including
state-sponsored ones, are weaponizing information on Online Social Networks to
run false information campaigns with targeted manipulation of public opinion on
specific topics. These false information campaigns can have dire consequences
to the public: mutating their opinions and actions, especially with respect to
critical world events like major elections. Evidently, the problem of false
information on the Web is a crucial one, and needs increased public awareness,
as well as immediate attention from law enforcement agencies, public
institutions, and in particular, the research community. In this paper, we make
a step in this direction by providing a typology of the Web's false information
ecosystem, comprising various types of false information, actors, and their
motives. We report a comprehensive overview of existing research on the false
information ecosystem by identifying several lines of work: 1) how the public
perceives false information; 2) understanding the propagation of false
information; 3) detecting and containing false information on the Web; and 4)
false information on the political stage. In this work, we pay particular
attention to political false information as: 1) it can have dire consequences
to the community (e.g., when election results are mutated) and 2) previous work
show that this type of false information propagates faster and further when
compared to other types of false information. Finally, for each of these lines
of work, we report several future research directions that can help us better
understand and mitigate the emerging problem of false information dissemination
on the Web
Rumor Stance Classification in Online Social Networks: A Survey on the State-of-the-Art, Prospects, and Future Challenges
The emergence of the Internet as a ubiquitous technology has facilitated the
rapid evolution of social media as the leading virtual platform for
communication, content sharing, and information dissemination. In spite of
revolutionizing the way news used to be delivered to people, this technology
has also brought along with itself inevitable demerits. One such drawback is
the spread of rumors facilitated by social media platforms which may provoke
doubt and fear upon people. Therefore, the need to debunk rumors before their
wide spread has become essential all the more. Over the years, many studies
have been conducted to develop effective rumor verification systems. One aspect
of such studies focuses on rumor stance classification, which concerns the task
of utilizing users' viewpoints about a rumorous post to better predict the
veracity of a rumor. Relying on users' stances in rumor verification task has
gained great importance, for it has shown significant improvements in the model
performances. In this paper, we conduct a comprehensive literature review on
rumor stance classification in complex social networks. In particular, we
present a thorough description of the approaches and mark the top performances.
Moreover, we introduce multiple datasets available for this purpose and
highlight their limitations. Finally, some challenges and future directions are
discussed to stimulate further relevant research efforts.Comment: 13 pages, 2 figures, journa
Location Reference Recognition from Texts: A Survey and Comparison
A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs
Credibility analysis of textual claims with explainable evidence
Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources.
We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.Das Web ist eine riesige Quelle wertvoller Informationen, allerdings wurde es durch die Verbreitung von Falschmeldungen verschmutzt. Eine zunehmende Anzahl an Hoaxes, Falschmeldungen und irreführenden Informationen im Internet haben viele Websites hervorgebracht, auf denen die Fakten überprüft und zweifelhafte Behauptungen manuell bewertet werden. Die rasante Verbreitung großer Mengen von Fehlinformationen sind jedoch zum Engpass für die manuelle Überprüfung geworden. Dies erfordert Tools zur Bewertung der Glaubwürdigkeit, mit denen dieser Überprüfungsprozess automatisiert werden kann. In früheren Arbeiten in diesem Bereich werden starke Annahmen gemacht über die Struktur der Behauptungen und die Portale, in denen sie gepostet werden. Vor allem aber können die Black-Box-Techniken, die in früheren Arbeiten vorgeschlagen wurden, nicht erklären, warum eine bestimmte Aussage als glaubwürdig erachtet wird oder nicht. Um diesen Einschränkungen zu begegnen, wird in dieser Dissertation ein allgemeines Framework für die automatisierte Bewertung der Glaubwürdigkeit vorgeschlagen, bei dem keine Annahmen über die Struktur oder den Ursprung der Behauptungen gemacht werden. Insbesondere schlagen wir ein featurebasiertes Modell vor, das automatisch relevante Artikel zu einer bestimmten Behauptung abruft und deren Glaubwürdigkeit bewertet, indem die gegenseitige Interaktion zwischen dem Sprachstil der relevanten Artikel, ihre Haltung zur Behauptung und der Vertrauenswürdigkeit der zugrunde liegenden Quellen erfasst wird. Wir verbessern unseren Ansatz zur Bewertung der Glaubwürdigkeit weiter und schlagen ein auf neuronalen Netzen basierendes Modell vor. Im Gegensatz zum featurebasierten Modell ist dieses Modell nicht auf Feature-Engineering und externe Lexika angewiesen. Unsere beiden Modelle machen ihre Einschätzungen interpretierbar, indem sie erklärbare Beweise aus sorgfältig ausgewählten Webquellen extrahieren. Wir verwenden unsere Modelle zur Entwicklung eines Webinterfaces, CredEye, mit dem Benutzer die Glaubwürdigkeit einer Behauptung in Textform automatisch bewerten und verstehen können, indem sie automatisch ausgewählte Beweisstücke einsehen. Darüber hinaus untersuchen wir das Problem der Positionsklassifizierung und schlagen ein auf neuronalen Netzen basierendes Modell vor, um die Position verschiedener Benutzerperspektiven in Bezug auf die umstrittenen Behauptungen vorherzusagen. Bei einer kontroversen Behauptung und einem Benutzerkommentar sagt unser Einstufungsmodell voraus, ob der Benutzerkommentar die Behauptung unterstützt oder ablehnt
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