SiMiD: similarity-based misinformation detection via communities on social media posts

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buir.contributor.authorÖzçelik, Oğuzhan
buir.contributor.authorCan, Fazlı
buir.contributor.orcidÖzçelik, Oğuzhan|0000-0002-9420-9854
buir.contributor.orcidCan, Fazlı|0000-0003-0016-4278
dc.citation.epage8en_US
dc.citation.spage1
dc.contributor.authorÖzçelik, Oğuzhan
dc.contributor.authorToraman, C.
dc.contributor.authorCan, Fazlı
dc.coverage.spatialAbu Dhabi, United Arab Emirates
dc.date.accessioned2024-03-07T08:09:41Z
dc.date.available2024-03-07T08:09:41Z
dc.date.issued2024-01-02
dc.departmentDepartment of Computer Engineering
dc.descriptionConference Name: 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)
dc.descriptionDate of Conference: 21-24 November 2023
dc.description.abstractSocial media users often find themselves exposed to similar viewpoints and tend to avoid contrasting opinions, particularly when connected within a community. In this study, we leverage the presence of communities in misinformation detection on social media. For this purpose, we propose a similarity-based method that utilizes user-follower interactions within a social network to identify and combat misinformation spread. The method first extracts important textual features of social media posts via contrastive learning and then measures the cosine similarity per social media post based on their relevance to each user in the community. Next, we train a classifier to assess the truthfulness of social media posts using these similarity scores. We evaluate our approach on three real-world datasets and compare our method with six baselines. The experimental results and statistical tests show that contrastive learning and leveraging communities can effectively enhance the detection of misinformation on social media.
dc.description.provenanceMade available in DSpace on 2024-03-07T08:09:41Z (GMT). No. of bitstreams: 1 SiMiD_Similarity-based_misinformation_detection_via_communities_on_social_media_posts.pdf: 994797 bytes, checksum: bd43d650c19dfece979bce91136652f4 (MD5) Previous issue date: 2024-01-02en
dc.identifier.doi10.1109/SNAMS60348.2023.10375480
dc.identifier.eisbn979-8-3503-1890-6
dc.identifier.eissn2831-7343
dc.identifier.isbn979-8-3503-2995-7
dc.identifier.issn2831-7351
dc.identifier.urihttps://hdl.handle.net/11693/114375
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isversionofhttps://dx.doi.org/10.1109/SNAMS60348.2023.10375480
dc.source.title2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)
dc.subjectMisinformation detection
dc.subjectSocial networks
dc.subjectSocial media analysis
dc.subjectCommunity detection
dc.subjectTransformers
dc.titleSiMiD: similarity-based misinformation detection via communities on social media posts
dc.typeConference Paper

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