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Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision (2210.10031v2)

Published 18 Oct 2022 in cs.CL, cs.CY, cs.LG, and cs.SI

Abstract: In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

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References (64)
  1. D. Cucinotta and M. Vanelli, “Who declares covid-19 a pandemic,” Acta Bio Medica: Atenei Parmensis, 2020.
  2. F. Tagliabue, L. Galassi, and P. Mariani, “The “pandemic” of disinformation in covid-19,” SN comprehensive clinical medicine, 2020.
  3. S. McLeod, “Maslow’s hierarchy of needs,” Simply psychology, 2007.
  4. J. Haidt and C. Joseph, “Intuitive ethics: How innately prepared intuitions generate culturally variable virtues,” Daedalus, 2004.
  5. J. Haidt and J. Graham, “When morality opposes justice: Conservatives have moral intuitions that liberals may not recognize,” SJR, 2007.
  6. J. Graham, J. Haidt, and B. A. Nosek, “Liberals and conservatives rely on different sets of moral foundations.,” JPSP, 2009.
  7. J. Graham, J. Haidt, et al., “Moral foundations theory: The pragmatic validity of moral pluralism,” in Adv Exp Soc Psychol, 2013.
  8. C. R. Weber and C. M. Federico, “Moral foundations and heterogeneity in ideological preferences,” Political Psychology, 2013.
  9. J. R. Silver and E. Silver, “Why are conservatives more punitive than liberals? a moral foundations approach.,” LHB, 2017.
  10. K. Johnson and D. Goldwasser, “Classification of moral foundations in microblog political discourse,” in ACL, 2018.
  11. S. Roy, M. L. Pacheco, and D. Goldwasser, “Identifying morality frames in political tweets using relational learning,” in EMNLP, 2021.
  12. F. M. Turner-Zwinkels, B. B. Johnson, et al., “Conservatives’ moral foundations are more densely connected than liberals’ moral foundations,” PSPB, 2021.
  13. D. Wawrzuta, M. Jaworski, et al., “What arguments against covid-19 vaccines run on facebook in poland: Content analysis of comments,” Vaccines, 2021.
  14. M. Weinzierl, S. Hopfer, and S. M. Harabagiu, “Misinformation adoption or rejection in the era of covid-19,” in ICWSM, 2021.
  15. M. L. Pacheco, T. Islam, M. Mahajan, A. Shor, M. Yin, L. Ungar, and D. Goldwasser, “A holistic framework for analyzing the covid-19 vaccine debate,” NAACL, 2022.
  16. S. Pagliaro, S. Sacchi, et al., “Trust predicts covid-19 prescribed and discretionary behavioral intentions in 23 countries,” PloS one, 2021.
  17. R. Díaz and F. Cova, “Reactance, morality, and disgust: The relationship between affective dispositions and compliance with official health recommendations during the covid-19 pandemic,” Cognition and Emotion, 2021.
  18. E. Y. Chan, “Moral foundations underlying behavioral compliance during the covid-19 pandemic,” Personality and individual differences, 2021.
  19. C. Shurafa, K. Darwish, and W. Zaghouani, “Political framing: Us covid19 blame game,” in SocInfo, 2020.
  20. G. Muric, Y. Wu, et al., “Covid-19 vaccine hesitancy on social media: building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies,” JMIR, 2021.
  21. M. Thelwall, K. Kousha, and S. Thelwall, “Covid-19 vaccine hesitancy on english-language twitter,” EPI, 2021.
  22. K. Krawczyk, T. Chelkowski, , et al., “Quantifying online news media coverage of the covid-19 pandemic: Text mining study and resource,” JMIR, 2021.
  23. E. Jing and Y.-Y. Ahn, “Characterizing partisan political narratives about covid-19 on twitter,” arXiv preprint arXiv:2103.06960, 2021.
  24. M. F. Prado, V. P. Bustos, et al., “Moral narratives around the vaccination discourse on the facebook platform,” arXiv preprint arXiv:2206.01598, 2022.
  25. J. Shi, P. Ghasiya, et al., “Psycho-linguistic differences among competing vaccination communities on social media,” APSIPA, 2021.
  26. S. E. Bokemper, G. A. Huber, et al., “Testing persuasive messaging to encourage covid-19 risk reduction,” PloS one, 2022.
  27. K. Mermin-Bunnell and W.-k. Ahn, “It’s time to be disgusting about covid-19: Effect of disgust priming on covid-19 public health compliance among liberals and conservatives,” Plos one, 2022.
  28. X. Nan, I. A. Iles, et al., “Public health messaging during the covid-19 pandemic and beyond: Lessons from communication science,” Health Communication, 2022.
  29. A. Graham, F. T. Cullen, et al., “Faith in trump, moral foundations, and social distancing defiance during the coronavirus pandemic,” Socius, 2020.
  30. F. Heine and E. Wolters, “Using moral foundations in government communication to reduce vaccine hesitancy,” PloS one, 2021.
  31. H. Tarry, V. Vézina, J. Bailey, and L. Lopes, “Political orientation, moral foundations, and covid-19 social distancing,” PloS one, 2022.
  32. G. L. Coelho, L. J. Wolf, et al., “Do left-wingers discriminate? a cross-cultural study on the links between political orientation, values, moral foundations, and the covid-19 passport,” PsyArXiv., 2022.
  33. E. Kyung, M. Thomas, and A. Krishna, “How political identity influences covid-19 risk perception: A model of identity-based risk perception,” JACR, 2022.
  34. Y. Mejova and K. Kalimeri, “Covid-19 on facebook ads: competing agendas around a public health crisis,” in SIGCAS, 2020.
  35. M. Silva and F. Benevenuto, “Covid-19 ads as political weapon,” in SAC, 2021.
  36. C. Belém, V. Balayan, P. Saleiro, and P. Bizarro, “Weakly supervised multi-task learning for concept-based explainability,” arXiv preprint arXiv:2104.12459, 2021.
  37. D. Mekala, X. Zhang, and J. Shang, “Meta: Metadata-empowered weak supervision for text classification,” in EMNLP, 2020.
  38. A. Ratner, B. Hancock, et al., “Snorkel metal: Weak supervision for multi-task learning,” in DEEM, 2018.
  39. T. Islam and D. Goldwasser, “Twitter user representation using weakly supervised graph embedding,” in ICWSM, 2022.
  40. R. Caruana, “Multitask learning,” Machine learning, 1997.
  41. P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv preprint arXiv:1605.05101, 2016.
  42. X. Liu, P. He, et al., “Multi-task deep neural networks for natural language understanding,” in ACL, 2019.
  43. G. Lu, J. Gan, et al., “Multi-task learning using a hybrid representation for text classification,” Neural Comput. Appl., 2020.
  44. S. Vandenhende, S. Georgoulis, et al., “Multi-task learning for dense prediction tasks: A survey,” TPAMI, 2021.
  45. J. Cohen, “A coefficient of agreement for nominal scales,” EPM, 1960.
  46. M. Weinzierl and S. Harabagiu, “From hesitancy framings to vaccine hesitancy profiles: A journey of stance, ontological commitments and moral foundations,” arXiv preprint arXiv:2202.09456, 2022.
  47. F. K. Cheng, “Debate on mandatory covid-19 vaccination,” Ethics, Medicine and Public Health, 2022.
  48. D. M. Ehde, M. K. Roberts, et al., “Covid-19 vaccine hesitancy in adults with multiple sclerosis in the united states: a follow up survey during the initial vaccine rollout in 2021,” MSARD, 2021.
  49. J. Roozenbeek, C. R. Schneider, et al., “Susceptibility to misinformation about covid-19 around the world,” R. Soc. Open Sci., 2020.
  50. N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” arXiv preprint arXiv:1908.10084, 2019.
  51. P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math, 1987.
  52. L. van der Maaten and G. Hinton JMLR, 2008.
  53. J. W. Pennebaker, M. E. Francis, and R. J. Booth, “Linguistic inquiry and word count: Liwc 2001,” Mahway: Lawrence Erlbaum Associates, 2001.
  54. Y. R. Tausczik and J. W. Pennebaker, “The psychological meaning of words: Liwc and computerized text analysis methods,” JLS, 2010.
  55. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, 1997.
  56. M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., 1997.
  57. Y. Bengio, R. Ducharme, and P. Vincent, “A neural probabilistic language model,” NeurIPS, 2000.
  58. J. Devlin, M.-W. Chang, et al., “Bert: Pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT, 2019.
  59. V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML, 2010.
  60. N. Srivastava, G. Hinton, et al., “Dropout: a simple way to prevent neural networks from overfitting,” JMLR, 2014.
  61. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR (Poster), 2015.
  62. Student, “Probable error of a correlation coefficient,” Biometrika, 1908.
  63. W. G. Cochran, “The χ𝜒\chiitalic_χ2 test of goodness of fit,” The Annals of mathematical statistics, 1952.
  64. Y.-W. Cheung and K. S. Lai, “Lag order and critical values of the augmented dickey–fuller test,” JBES, 1995.
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Authors (2)
  1. Tunazzina Islam (15 papers)
  2. Dan Goldwasser (48 papers)
Citations (8)
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