mlsanityData to 7 Oct 2026

TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting

arXiv 2102.00509 ↗First posted 31 Jan 2021cs.SI cs.CLcited by 4

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Abstract (arXiv)

We present TruthBot, an all-in-one multilingual conversational chatbot designed for seeking truth (trustworthy and verified information) on specific topics. It helps users to obtain information specific to certain topics, fact-check information, and get recent news. The chatbot learns the intent of a query by training a deep neural network from the data of the previous intents and responds appropriately when it classifies the intent in one of the classes above. Each class is implemented as a separate module that uses either its own curated knowledge-base or searches the web to obtain the correct information. The topic of the chatbot is currently set to COVID-19. However, the bot can be easily customized to any topic-specific responses. Our experimental results show that each module performs significantly better than its closest competitor, which is verified both quantitatively and through several user-based surveys in multiple languages. TruthBot has been deployed in June 2020 and is currently running.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 31 Jan 2021.

Builds on

8 of 23 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  4. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  5. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  6. 2018From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbotscited by 85
  7. 2019Network-based Fake News Detection: A Pattern-driven Approachcited by 33
  8. 2017Evaluating Quality of Chatbots and Intelligent Conversational Agentscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

4 papers.