DSC180B-Misinformation

View the Project on GitHub anaaamika/DSC180B-Misinformation

Introduction Data Misinformation Model Topic Modeling Sentiment Analysis References

References

  1. Barthel M, Mitchell A, Holcomb J. Many Americans Believe Fake News Is Sowing Confusion; 2016. Available from: http://www.journalism.org/2016/12/15/many-americans-believe-fake-news-is-sowing-confusion/.
  2. N. Mohan, “Perspective: Tackling Misinformation on YouTube,” blog.youtube.com, Aug. 25, 2021. [Online]. Available: https://blog.youtube/inside-youtube/tackling-misinfo/. [Accessed: Dec. 4, 2021].
  3. Y. Roth, N. Pickles, “Updating our approach to misleading information,” blog.twitter.com, May 11, 2020. [Online]. Available: https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information. [Accessed: Dec. 4, 2021].
  4. D. Allington, B. Duffy, S. Wessely, N. Dhavan, J. Rubin, “Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency,” Psychological Medicine, vol. 51, no. 10, pp. 1763–1769, 2021.
  5. A. Knuutila, A. Herasimenka, H. Au, J. Bright, R. Nielsen, P. Howard, “COVID-Related Misinformation on YouTube: The Spread of Misinformation Videos on Social Media and the Effectiveness of Platform Policies,” COMPROP Data Memo, vol. 6, pp. 1-7, 2020.
  6. K. Shu, A. Sliva, S. Wang, J. Tang, & H. Liu, “Fake News Detection on Social Media: A Data Mining Perspective,” Sigkdd Explorations, vol. 19, no. 1, pp. 22–36, 2017
  7. R. Jagtap, A. Kumar, R. Goel, S. Sharma, R. Sharma, C. George, “Misinformation Detection on YouTube Using Video Captions,” 2021.
  8. M. N. Hussain, S. Tokdemir, N. Agarwal and S. Al-Khateeb, “Analyzing Disinformation and Crowd Manipulation Tactics on YouTube,” 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1092-1095, 2018.
  9. J. Pennington, R. Socher, C. Manning, “GloVe: Global Vectors for Word Representation,” nlp.stanford.edu, 2014. [Online]. Available :https://nlp.stanford.edu/projects/glove/. [Accessed: Dec. 4, 2021].
  10. Banda, J, “A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration”, 2021. Available from: https://doi.org/10.3390/epidemiologia2030024.
  11. Bisaillon, C, “Fake and real news dataset: Classifying the news”, 2021. Available from: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset.