Social media networks: Rich on-line data sources

This chapter illustrates how social media networks can be harnessed for research to highlight feelings, behaviour and opinions of customers. This is a new area of research and will include discussions on data mining and thematic analysis

Distant Supervision for Tweet Classification Using YouTube Labels

We study an approach to tweet classification based on distant supervision, whereby we automatically transfer labels from one social medium to another. In particular, we apply classes assigned to YouTube videos to tweets linking to these videos. This provides for free a vir-tually unlimited number of labelled instances that can be used as training data. The experiments we have run show that a tweet classifier trained via these automati-cally labelled data substantially outperforms an analo-gous classifier trained with a limited amount of manu-ally labelled data

Understanding online political networks: The case of the far-right and far-left in Greece

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper examines the connectivity among political networks on Twitter. We explore dynamics inside and between the far right and the far left, as well as the relation between the structure of the network and sentiment. The 2015 Greek political context offers a unique opportunity to investigate political communication in times of political intensity and crisis. We explore interactions inside and between political networks on Twitter in the run up to the elections of three different ballots: the parliamentary election of 25 January, the bailout referendum of 5 July, the snap election of 20 September; we, then, compare political action during campaigns with that during routinized politics.This work received funding from the European Union Horizon 2020 Programme (Horizon2020/2014–2020), under grant agreement 688380

Sentiment Analysis for Fake News Detection

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150

Viewpoint Discovery and Understanding in Social Networks

The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a - usually controversial - topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints

A Survey on Automated Fact-Checking

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how factchecking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research