TY - GEN
T1 - Multi-modal Analysis of Misleading Political News
AU - Shrestha, Anu
AU - Spezzano, Francesca
AU - Gurunathan, Indhumathi
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The internet is a valuable resource to openly share information or opinions. Unfortunately, such internet openness has also made it increasingly easy to abuse these platforms through the dissemination of misinformation. As people are generally awash in information, they can sometimes have difficulty discerning misinformation propagated on these web platforms from truthful information. They may also lean too heavily on information providers or social media platforms to curate information even though such providers do not commonly validate sources. In this paper, we focus on political news and present an analysis of misleading news according to different modalities, including news content (headline, body, and associated image) and source bias. Our findings show that hyperpartisan news sources are more likely to spread misleading stories than other sources and that it is not necessary to read news body content to assess its validity, but considering other modalities such as headlines, visual content, and publisher bias can achieve better performances.
AB - The internet is a valuable resource to openly share information or opinions. Unfortunately, such internet openness has also made it increasingly easy to abuse these platforms through the dissemination of misinformation. As people are generally awash in information, they can sometimes have difficulty discerning misinformation propagated on these web platforms from truthful information. They may also lean too heavily on information providers or social media platforms to curate information even though such providers do not commonly validate sources. In this paper, we focus on political news and present an analysis of misleading news according to different modalities, including news content (headline, body, and associated image) and source bias. Our findings show that hyperpartisan news sources are more likely to spread misleading stories than other sources and that it is not necessary to read news body content to assess its validity, but considering other modalities such as headlines, visual content, and publisher bias can achieve better performances.
KW - Misinformation detection on the web
KW - Multi-modal content analysis
KW - Source bias
UR - https://www.scopus.com/pages/publications/85096418037
U2 - 10.1007/978-3-030-61841-4_18
DO - 10.1007/978-3-030-61841-4_18
M3 - Conference contribution
SN - 9783030618407
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 261
EP - 276
BT - Disinformation in Open Online Media - 2nd Multidisciplinary International Symposium, MISDOOM 2020, Proceedings
A2 - van Duijn, Max
A2 - Preuss, Mike
A2 - Takes, Frank
A2 - Verberne, Suzan
A2 - Spaiser, Viktoria
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media, MISDOOM 2020
Y2 - 26 October 2020 through 27 October 2020
ER -