Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit
In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian state media outlets output torrents of misleading and outright false information. In this work, we study this coordinated information campaign in order to understand the most prominent state media...
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| creator | Hanley, Hans W. A Kumar, Deepak Durumeric, Zakir |
| description | In the buildup to and in the weeks following the Russian Federation's
invasion of Ukraine, Russian state media outlets output torrents of misleading
and outright false information. In this work, we study this coordinated
information campaign in order to understand the most prominent state media
narratives touted by the Russian government to English-speaking audiences. To
do this, we first perform sentence-level topic analysis using the
large-language model MPNet on articles published by ten different pro-Russian
propaganda websites including the new Russian "fact-checking" website
waronfakes.com. Within this ecosystem, we show that smaller websites like
katehon.com were highly effective at publishing topics that were later echoed
by other Russian sites. After analyzing this set of Russian information
narratives, we then analyze their correspondence with narratives and topics of
discussion on the r/Russia and 10 other political subreddits. Using MPNet and a
semantic search algorithm, we map these subreddits' comments to the set of
topics extracted from our set of Russian websites, finding that 39.6% of
r/Russia comments corresponded to narratives from pro-Russian propaganda
websites compared to 8.86% on r/politics. |
| doi_str_mv | 10.48550/arxiv.2205.14484 |
| format | Article |
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invasion of Ukraine, Russian state media outlets output torrents of misleading
and outright false information. In this work, we study this coordinated
information campaign in order to understand the most prominent state media
narratives touted by the Russian government to English-speaking audiences. To
do this, we first perform sentence-level topic analysis using the
large-language model MPNet on articles published by ten different pro-Russian
propaganda websites including the new Russian "fact-checking" website
waronfakes.com. Within this ecosystem, we show that smaller websites like
katehon.com were highly effective at publishing topics that were later echoed
by other Russian sites. After analyzing this set of Russian information
narratives, we then analyze their correspondence with narratives and topics of
discussion on the r/Russia and 10 other political subreddits. Using MPNet and a
semantic search algorithm, we map these subreddits' comments to the set of
topics extracted from our set of Russian websites, finding that 39.6% of
r/Russia comments corresponded to narratives from pro-Russian propaganda
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invasion of Ukraine, Russian state media outlets output torrents of misleading
and outright false information. In this work, we study this coordinated
information campaign in order to understand the most prominent state media
narratives touted by the Russian government to English-speaking audiences. To
do this, we first perform sentence-level topic analysis using the
large-language model MPNet on articles published by ten different pro-Russian
propaganda websites including the new Russian "fact-checking" website
waronfakes.com. Within this ecosystem, we show that smaller websites like
katehon.com were highly effective at publishing topics that were later echoed
by other Russian sites. After analyzing this set of Russian information
narratives, we then analyze their correspondence with narratives and topics of
discussion on the r/Russia and 10 other political subreddits. Using MPNet and a
semantic search algorithm, we map these subreddits' comments to the set of
topics extracted from our set of Russian websites, finding that 39.6% of
r/Russia comments corresponded to narratives from pro-Russian propaganda
websites compared to 8.86% on r/politics.</description><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Social and Information Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotkMtOwzAQRb1hgQofwIr5gYTEj9RhhyqgSIVKbSqW0cQParV1Isctj68nDdzN3CvNjHQPITd5lnIpRHaH4cudUkozkeacS35JPufYdcb3Eb0y97CJbu9-nP-AtTmgj04NBoPaQmyhCqh2sDr2vUMP64jRwKvRDuENQ8DoTqYHbNpjhLg142KbbHYBnT8fvGOApYeV0drFK3Jhcd-b6_85IdXTYzWbJ4vl88vsYZFgMeWJxsZOpZBIBWu04RmTxtqcDeJCslLRcqhBlbGYa65tWfCGFqxkSg9ZaDYht39vx-Z1F9wBw3d9JlCPBNgvbyZXjA</recordid><startdate>20220528</startdate><enddate>20220528</enddate><creator>Hanley, Hans W. A</creator><creator>Kumar, Deepak</creator><creator>Durumeric, Zakir</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220528</creationdate><title>Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit</title><author>Hanley, Hans W. A ; Kumar, Deepak ; Durumeric, Zakir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-dabf7858a253bde4038eff1333345839c294482cefa1d4df964b26393cd1d45d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Social and Information Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Hanley, Hans W. A</creatorcontrib><creatorcontrib>Kumar, Deepak</creatorcontrib><creatorcontrib>Durumeric, Zakir</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hanley, Hans W. A</au><au>Kumar, Deepak</au><au>Durumeric, Zakir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit</atitle><date>2022-05-28</date><risdate>2022</risdate><abstract>In the buildup to and in the weeks following the Russian Federation's
invasion of Ukraine, Russian state media outlets output torrents of misleading
and outright false information. In this work, we study this coordinated
information campaign in order to understand the most prominent state media
narratives touted by the Russian government to English-speaking audiences. To
do this, we first perform sentence-level topic analysis using the
large-language model MPNet on articles published by ten different pro-Russian
propaganda websites including the new Russian "fact-checking" website
waronfakes.com. Within this ecosystem, we show that smaller websites like
katehon.com were highly effective at publishing topics that were later echoed
by other Russian sites. After analyzing this set of Russian information
narratives, we then analyze their correspondence with narratives and topics of
discussion on the r/Russia and 10 other political subreddits. Using MPNet and a
semantic search algorithm, we map these subreddits' comments to the set of
topics extracted from our set of Russian websites, finding that 39.6% of
r/Russia comments corresponded to narratives from pro-Russian propaganda
websites compared to 8.86% on r/politics.</abstract><doi>10.48550/arxiv.2205.14484</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computers and Society Computer Science - Learning Computer Science - Social and Information Networks |
| title | Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit |
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