Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

An Interactive Framework for Profiling News Media Sources (2309.07384v2)

Published 14 Sep 2023 in cs.CL

Abstract: The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs. While detecting and profiling the sources that spread this news is important to maintain a healthy society, it is challenging for automated systems. In this paper, we propose an interactive framework for news media profiling. It combines the strengths of graph based news media profiling models, Pre-trained LLMs, and human insight to characterize the social context on social media. Experimental results show that with as little as 5 human interactions, our framework can rapidly detect fake and biased news media, even in the most challenging settings of emerging news events, where test data is unseen.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. FLAIR: An easy-to-use framework for state-of-the-art NLP. In NAACL 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 54–59.
  2. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862.
  3. Predicting factuality of reporting and bias of news media sources. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’18, Brussels, Belgium.
  4. What was written vs. who read it: News media profiling using text analysis and social media context. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL ’20.
  5. Homophily and polarization in the age of misinformation. The European Physical Journal Special Topics, 225:2047–2059.
  6. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  7. Learning community embedding with community detection and node embedding on graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 377–386.
  8. The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118.
  9. Towards teachable reasoning systems: Using a dynamic memory of user feedback for continual system improvement. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9465–9480.
  10. Yosh Halberstam and Brian Knight. 2016. Homophily, group size, and the diffusion of political information in social networks: Evidence from twitter. Journal of public economics, 143:73–88.
  11. Dafd: Domain adaptation framework for fake news detection. In International Conference on Neural Information Processing, pages 305–316. Springer.
  12. Reward design with language models. In The Eleventh International Conference on Learning Representations.
  13. Chang Li and Dan Goldwasser. 2019. Encoding social information with graph convolutional networks forpolitical perspective detection in news media. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2594–2604.
  14. Adadebunk: An efficient and reliable deep state space model for adaptive fake news early detection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 1156–1165.
  15. Detect rumors in microblog posts for low-resource domains via adversarial contrastive learning. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2543–2556.
  16. Quantifying and alleviating political bias in language models. Artificial Intelligence, 304:103654.
  17. Mitigating political bias in language models through reinforced calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 14857–14866.
  18. Yang Liu and Yi-Fang Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, volume 32.
  19. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  20. Nikhil Mehta and Dan Goldwasser. 2023. Interactively learning social media representations improves news source factuality detection. arXiv preprint arXiv:2309.14966.
  21. Tackling fake news detection by continually improving social context representations using graph neural networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1363–1380.
  22. Domain adaptive fake news detection via reinforcement learning. In Proceedings of the ACM Web Conference 2022, pages 3632–3640.
  23. Fang: Leveraging social context for fake news detection using graph representation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 1165–1174.
  24. OpenAI. 2023. Gpt-4 technical report. ArXiv, abs/2303.08774.
  25. A holistic framework for analyzing the covid-19 vaccine debate. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5821–5839.
  26. Interactive concept learning for uncovering latent themes in large text collections. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5059–5080.
  27. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc.
  28. Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476.
  29. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. In The Eleventh International Conference on Learning Representations.
  30. Scarlet: explainable attention based graph neural network for fake news spreader prediction. In Pacific-Asia conference on knowledge discovery and data mining, pages 714–727. Springer.
  31. Detecting fake news spreaders in social networks using inductive representation learning. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 182–189. IEEE.
  32. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982–3992.
  33. Factoid: A new dataset for identifying misinformation spreaders and political bias. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3231–3241.
  34. Temporal graph analysis of misinformation spreaders in social media. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 89–104.
  35. Modeling relational data with graph convolutional networks. In European semantic web conference, pages 593–607. Springer.
  36. A comprehensive survey on community detection with deep learning. IEEE Transactions on Neural Networks and Learning Systems.
  37. The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3):554–559.
  38. The spread of true and false news online. Science, 359(6380):1146–1151.
  39. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315.
  40. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837.
  41. Evaluation of fake news detection with knowledge-enhanced language models. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 1425–1429.
  42. A survey of human-in-the-loop for machine learning. Future Generation Computer Systems.
  43. Entity-aware dual co-attention network for fake news detection. In Findings of the Association for Computational Linguistics: EACL 2023, pages 106–113.
  44. Early detection of fake news by utilizing the credibility of news, publishers, and users based on weakly supervised learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5444–5454.
  45. The wisdom of hindsight makes language models better instruction followers. In International Conference on Machine Learning, pages 41414–41428. PMLR.
  46. A survey of large language models. arXiv preprint arXiv:2303.18223.
  47. Generalizing to the future: Mitigating entity bias in fake news detection. In Proceedings of the 45nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery.
  48. Memory-guided multi-view multi-domain fake news detection. IEEE Transactions on Knowledge and Data Engineering.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Nikhil Mehta (34 papers)
  2. Dan Goldwasser (48 papers)
Citations (4)
View on Semantic Scholar