About this Research Topic
In recent years, hate speech and fake news on social media platforms are major issues that affect both the platforms themselves and the communities in which they are used. Hate speech and fake news are serious issues that extend beyond the virtual boundaries of social media platforms.
Due to the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech and fake news has become a major concern for society, individuals, policymakers, and researchers. The most pressing societal issues are combating hate speech and fake news. Recent advancements in Natural Language Processing (NLP) and Natural Language Understanding (NLU) enable the detection of hate speech and fake news in textual streams to be more accurate.
This Research Topic also focuses on different social media platform messages, specifically hateful, xenophobic, and fake news multimodal contents. The topic also welcomes contributions focused on the creation of a new dataset classification for hate speech and fake news comprised of the selected social media IDs, as well as the code to generate their visual appearance as if rendered in a web browser accordingly.
We would like to address the following specific themes in the article collection:
- Identification of the implemented techniques to countering such speeches and news (inclusive of survey and review papers)
- Machine learning and NLP models for detecting or analysing cyberbullying, hate speech, and fake news propagation
- Identification of counter speeches and news
- Preparation of corpora for the counter speeches and news
- Annotation of counter speeches and news
- Evaluation of the efficiency of counter speech and news
- Differences in counter-narrative and alternative narrative in both speeches and news
- Presence of alternative narratives in online debates, threads for the speeches, and news
- Automatic detection of counter speeches and news
- Mixed approaches and automatic detection of counter speeches and news
-Social media vs fake news: challenges and countermeasures
- Machine learning techniques' resistance to adversarial attacks in the detection of bots and fake news
- Models for detecting and mitigating online deception and propaganda
- Perspectives of Natural Language Processing for AI, Linguistics and Cognitive Science for fake news and hate speech detection
Due to the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech and fake news has become a major concern for society, individuals, policymakers, and researchers. The most pressing societal issues are combating hate speech and fake news. Recent advancements in Natural Language Processing (NLP) and Natural Language Understanding (NLU) enable the detection of hate speech and fake news in textual streams to be more accurate.
This Research Topic also focuses on different social media platform messages, specifically hateful, xenophobic, and fake news multimodal contents. The topic also welcomes contributions focused on the creation of a new dataset classification for hate speech and fake news comprised of the selected social media IDs, as well as the code to generate their visual appearance as if rendered in a web browser accordingly.
We would like to address the following specific themes in the article collection:
- Identification of the implemented techniques to countering such speeches and news (inclusive of survey and review papers)
- Machine learning and NLP models for detecting or analysing cyberbullying, hate speech, and fake news propagation
- Identification of counter speeches and news
- Preparation of corpora for the counter speeches and news
- Annotation of counter speeches and news
- Evaluation of the efficiency of counter speech and news
- Differences in counter-narrative and alternative narrative in both speeches and news
- Presence of alternative narratives in online debates, threads for the speeches, and news
- Automatic detection of counter speeches and news
- Mixed approaches and automatic detection of counter speeches and news
-Social media vs fake news: challenges and countermeasures
- Machine learning techniques' resistance to adversarial attacks in the detection of bots and fake news
- Models for detecting and mitigating online deception and propaganda
- Perspectives of Natural Language Processing for AI, Linguistics and Cognitive Science for fake news and hate speech detection
Keywords: Fake news, Hate Speech, Social Media Platforms, Multimodal Forms, Countering Methods
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
