Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
TLDR
Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
Citations
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Journal ArticleDOI
False information detection in online content and its role in decision making: a systematic literature review
TL;DR: This work conducted a systematic literature review of detecting false information and its role in decision making spread across online content and describes four deep learning and eight machine learning techniques for false information detection.
Posted Content
Decision support with text-based emotion recognition: Deep learning for affective computing
TL;DR: This work adapts recurrent neural networks from the field of deep learning to affective computing and extends these networks for predicting the score of different affective dimensions, and implements transfer learning for pre-training word embeddings.
Journal ArticleDOI
Interoperable pipelines for social cyber-security: assessing Twitter information operations during NATO Trident Juncture 2018
TL;DR: This work advocates the use of interoperable pipelines of computational tools for accumulating and triangulating insights that enable social cyber-security analysts to draw relevant insights across various scales of granularity.
Book ChapterDOI
CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
Alberto Barrón-Cedeño,Tamer Elsayed,Preslav Nakov,Giovanni Da San Martino,Maram Hasanain,Reem Suwaileh,Fatima Haouari +6 more
TL;DR: The CheckThat! Lab as discussed by the authors proposed four complementary tasks and a related task from previous lab editions, offered in English, Arabic, and Spanish, to predict which tweets in a Twitter stream are worth fact-checking.
Proceedings ArticleDOI
Multi-Label Fake News Detection using Multi-layered Supervised Learning
TL;DR: A novel method of multilevel multiclass fake news detection based on relabeling of the dataset and learning iteratively is proposed which outperforms the benchmark and experiments indicate that profile of the source of information contributes the most infake news detection.
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