<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bozhanova-etal-2021-predicting">
    <titleInfo>
        <title>Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Krasimira</namePart>
        <namePart type="family">Bozhanova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yoan</namePart>
        <namePart type="family">Dinkov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="family">Koychev</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Castaldo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tommaso</namePart>
        <namePart type="family">Venturini</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Preslav</namePart>
        <namePart type="family">Nakov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Held Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels. In particular, we design a rich set of features derived from the temporal evolution of the number of views, likes, dislikes, and comments for a video, which we then aggregate to the channel level. We develop and release a dataset for the task, containing observations of user attention on YouTube channels for 489 news media. Our experiments demonstrate both complementarity and sizable improvements over state-of-the-art textual representations.</abstract>
    <identifier type="citekey">bozhanova-etal-2021-predicting</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-1.22</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>182</start>
            <end>189</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels
%A Bozhanova, Krasimira
%A Dinkov, Yoan
%A Koychev, Ivan
%A Castaldo, Maria
%A Venturini, Tommaso
%A Nakov, Preslav
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F bozhanova-etal-2021-predicting
%X We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels. In particular, we design a rich set of features derived from the temporal evolution of the number of views, likes, dislikes, and comments for a video, which we then aggregate to the channel level. We develop and release a dataset for the task, containing observations of user attention on YouTube channels for 489 news media. Our experiments demonstrate both complementarity and sizable improvements over state-of-the-art textual representations.
%U https://aclanthology.org/2021.ranlp-1.22
%P 182-189

Markdown (Informal)

[Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels](https://aclanthology.org/2021.ranlp-1.22) (Bozhanova et al., RANLP 2021)

• Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels (Bozhanova et al., RANLP 2021)

ACL

• Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, and Preslav Nakov. 2021. Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 182–189, Held Online. INCOMA Ltd..