Detecting and identifying the reasons for deleted tweets before they are posted.

Autor:	Mubarak H; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar., Abdaljalil S; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar., Nassar A; College of Humanities and Social Sciences, Hamad Bin Khalifa University, Doha, Qatar., Alam F; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2023 Sep 29; Vol. 6, pp. 1219767. Date of Electronic Publication: 2023 Sep 29 (Print Publication: 2023).
DOI:	10.3389/frai.2023.1219767
Abstrakt:	Social media platforms empower us in several ways, from information dissemination to consumption. While these platforms are useful in promoting citizen journalism, public awareness, etc., they have misuse potential. Malicious users use them to disseminate hate speech, offensive content, rumor, etc. to promote social and political agendas or to harm individuals, entities, and organizations. Oftentimes, general users unconsciously share information without verifying it or unintentionally post harmful messages. Some of such content often gets deleted either by the platform due to the violation of terms and policies or by users themselves for different reasons, e.g., regret. There is a wide range of studies in characterizing, understanding, and predicting deleted content. However, studies that aim to identify the fine-grained reasons (e.g., posts are offensive, hate speech, or no identifiable reason) behind deleted content are limited. In this study, we address an existing gap by identifying and categorizing deleted tweets, especially within the Arabic context. We label them based on fine-grained disinformation categories. We have curated a dataset of 40K tweets, annotated with both coarse and fine-grained labels. Following this, we designed models to predict the likelihood of tweets being deleted and to identify the potential reasons for their deletion. Our experiments, conducted using a variety of classic and transformer models, indicate that performance surpasses the majority baseline (e.g., 25% absolute improvement for fine-grained labels). We believe that such models can assist in moderating social media posts even before they are published. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Mubarak, Abdaljalil, Nassar and Alam.)
Databáze:	MEDLINE
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