490
Views
0
CrossRef citations to date
0
Altmetric
RESEARCH ARTICLE

Algorithmic enhancements to identify predictable components from users’ data and a framework to detect misinformation in social media

ORCID Icon &
Pages 112-126 | Received 06 Aug 2021, Accepted 05 Jul 2022, Published online: 18 Jul 2022

References

  • Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 3. https://doi.org/10.17705/1jais.00423
  • Ahmed, H., Traore, I., & Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), e9. https://doi.org/10.1002/spy2.9
  • Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., and Quattrociocchi, W. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093. https://doi.org/10.1371/journal.pone.0118093
  • Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
  • Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 1–14. https://doi.org/10.1038/s41467-018-07761-2
  • Bruns, A., & Burgess, J. (2014). Crisis communication in natural disasters: The Queensland floods and Christchurch earthquakes. In Bruns, A, Mahrt, M, Weller, K, Burgess, J, & Puschmann, C (Eds.). Twitter and Society [Digital Formations, 89, 373–384. https://eprints.qut.edu.au/66329/
  • Buntain, C., & Golbeck, J. (2017, November). Automatically identifying fake news in popular twitter threads. In 2017 IEEE international conference on smart cloud (smartCloud) (pp. 208–215). IEEE.
  • Burnap, P., Rana, O. F., Avis, N., Williams, M., Housley, W., Edwards, A., and Morgan, J., & Sloan, L. (2015). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change, 95, 96–108. https://doi.org/10.1016/j.techfore.2013.04.013
  • Chae, B. K. (2015). Insights from hashtag# supplychain and Twitter analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259. https://doi.org/10.1016/j.ijpe.2014.12.037
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794).
  • Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017, May). Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media (Vol. 11, No. 1).
  • Derczynski, L., & Bontcheva, K. (2014, July). Pheme: Veracity in digital social networks. In UMAP workshops.
  • Dixit, G., & Panigrahi, P. (2013). Investigating determinants of information technology investments by Indian firms. Journal of Information Technology Management, 24(3), 13. http://jitm.ubalt.edu/XXIV-3/article2.pdf
  • Dixit, G., & Panigrahi, P. (2014). Information technology impact and role of firm age and export activity: An emerging economy context. Journal of Global Information Technology Management, 17(3), 169–187. https://doi.org/10.1080/1097198X.2014.951295
  • Earle, P. S., Bowden, D. C., & Guy, M. (2012). Twitter earthquake detection: Earthquake monitoring in a social world. Annals of Geophysics, 54(6), 708–715. https://doi.org/10.3929/ethz-b-000364555
  • ECI. (2018). Electronic voting machine. Election Commission of India. https://eci.gov.in/faqs/evm/general-qa/electronic-voting-machine-r2/
  • Evangelos, K., Efthimios, T., & Konstantinos, T. (2013). Understanding the Predictive Power of Social Media. Internet Research, 23(5), 544–559.
  • Helmstetter, S., & Paulheim, H. (2021). Collecting a large scale dataset for classifying fake news tweets using weak supervision. Future Internet, 13(5), 114. https://doi.org/10.3390/fi13050114
  • Hoang, T. B. N., & Mothe, J. (2018). Predicting information diffusion on Twitter–analysis of predictive features. Journal of Computational Science, 28, 257–264. https://doi.org/10.1016/j.jocs.2017.10.010
  • Imran, M., Ofli, F., Caragea, D., & Torralba, A. (2020). Using ai and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Information Processing & Management, 57(5), 102261. https://doi.org/10.1016/j.ipm.2020.102261
  • Johnson, N. F., Leahy, R., Restrepo, N. J., Velasquez, N., Zheng, M., and Manrique, P. (2019). Hidden resilience and adaptive dynamics of the global online hate ecology. Nature, 573(7773), 261–265. https://doi.org/10.1038/s41586-019-1494-7
  • Kaufhold, M. A., & Reuter, C. (2016). The self-organization of digital volunteers across social media: The case of the 2013 European floods in Germany. Journal of Homeland Security and Emergency Management, 13(1), 137–166. https://doi.org/10.1515/jhsem-2015-0063
  • Kavanaugh, A. L., Fox, E. A., Sheetz, S. D., Yang, S., Li, L. T., Shoemaker, D. J., and Natsev, A., & Xie, L. (2012). Social media use by government: From the routine to the critical. Government Information Quarterly, 29(4), 480–491. https://doi.org/10.1016/j.giq.2012.06.002
  • Kongthon, A., Haruechaiyasak, C., Pailai, J., & Kongyoung, S. (2014). The role of social media during a natural disaster: A case study of the 2011 Thai Flood. International Journal of Innovation and Technology Management, 11(3), 1440012. https://doi.org/10.1142/S0219877014400124
  • Kovács, G., Alonso, P., & Saini, R. (2021). Challenges of hate speech detection in social media. SN Computer Science, 2(2), 1–15. https://doi.org/10.1007/s42979-021-00457-3
  • Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702. https://doi.org/10.1016/j.ejor.2016.10.031
  • Krippendorff, K. (2011). Computing krippendorff’s alpha-reliability. University of Pennsylvania. https://repository.upenn.edu/asc_papers/43
  • MacAvaney, S., Yao, H.-R., Yang, E., Russell, K., Goharian, N., and Frieder, O. (2019). Hate speech detection: Challenges and solutions. PloS one, 14(8), e0221152. https://doi.org/10.1371/journal.pone.0221152
  • Martinez-Rojas, M., Del Carmen Pardo-Ferreira, M., & Rubio-Romero, J. C. (2018). Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43, 196–208. https://doi.org/10.1016/j.ijinfomgt.2018.07.008
  • Mendoza, M., Poblete, B., & Castillo, C. (2010, July). Twitter under crisis: Can we trust what we RT? In Proceedings of the first workshop on social media analytics (pp. 71–79).
  • Meyer, D. (2006). The Truth of Truthiness. CBS News. https://www.cbsnews.com/news/the-truth-of-truthiness/
  • Mondal, M., Silva, L. A., Correa, D., & Benevenuto, F. (2018). Characterizing usage of explicit hate expressions in social media. New Review of Hypermedia and Multimedia, 24(2), 110–130. https://doi.org/10.1080/13614568.2018.1489001
  • Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65–68. https://doi.org/10.1038/nature14604
  • Murthy, D., & Longwell, S. A. (2013). Twitter and disasters: The uses of Twitter during the 2010 Pakistan floods. Information, Communication & Society, 16(6), 837–855. https://doi.org/10.1080/1369118X.2012.696123
  • Namtirtha, A., Dutta, A., Dutta, B., Sundararajan, A., & Simmhan, Y. (2021). Best influential spreaders identification using network global structural properties. Scientific Reports, 11(1), 1–15. https://doi.org/10.1038/s41598-021-81614-9
  • Panagiotopoulos, P., Barnett, J., Bigdeli, A. Z., & Sams, S. (2016). Social media in emergency management: Twitter as a tool for communicating risks to the public. Technological Forecasting and Social Change, 111, 86–96. https://doi.org/10.1016/j.techfore.2016.06.010
  • Park, C. S. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641–1648. https://doi.org/10.1016/j.chb.2013.01.044
  • Perez, S. (2018). Twitter’s doubling of character count from 140 to 280 had little impact on length of tweets. TechCrunch.com. https://techcrunch.com/2018/10/30/twitters-doubling-of-character-count-from-140-to-280-had-little-impact-on-length-of-tweets/
  • Pierri, F., Piccardi, C., & Ceri, S. (2020). A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter. EPJ Data Science, 9(1), 35. https://doi.org/10.1140/epjds/s13688-020-00253-8
  • Poblete, B., Guzmán, J., Maldonado, J., & Tobar, F. (2018). Robust detection of extreme events using Twitter: Worldwide earthquake monitoring. IEEE Transactions on Multimedia, 20(10), 2551–2561. https://doi.org/10.1109/TMM.2018.2855107
  • Salminen, J., Almerekhi, H., Milenković, M., Jung, S. G., An, J., Kwak, H., and & Jansen, B. J. (2018). Anatomy of online hate: Developing a taxonomy and machine learning models for identifying and classifying hate in online news media. In Twelfth international AAAI conference on web and social media.
  • Simon, T., Goldberg, A., & Adini, B. (2015). Socializing in emergencies—A review of the use of social media in emergency situations. International Journal of Information Management, 35(5), 609–619. https://doi.org/10.1016/j.ijinfomgt.2015.07.001
  • Smith, A., & Rainie, L. (2010). 8% of online Americans use twitter. Pew Research Centre. http://www.pewinternet.org/2010/12/09/8-of-online-americans-use-twitter/
  • Sutton, J., League, C., Sellnow, T. L., & Sellnow, D. D. (2015). Terse messaging and public health in the midst of natural disasters: The case of the boulder floods. Health Communication, 30(2), 135–143. https://doi.org/10.1080/10410236.2014.974124
  • Takahashi, B., Tandoc, E. C., Jr, & Carmichael, C. (2015). Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, 50, 392–398. https://doi.org/10.1016/j.chb.2015.04.020
  • Velichety, S., Ram, S., & Bockstedt, J. (2019). Quality assessment of peer-produced content in knowledge repositories using development and coordination activities. Journal of Management Information Systems, 36(2), 478–512. https://doi.org/10.1080/07421222.2019.1598692
  • Velichety, S., & Ram, S. (2021). Finding a needle in the haystack: recommending online communities on social media platforms using network and design science. Journal of the Association for Information Systems, 22(5), 1285–1310. https://doi.org/10.17705/1jais.00694
  • Vieweg, S., Hughes, A. L., Starbird, K., & Palen, L. (2010, April). Microblogging during two natural hazards events: What twitter may contribute to situational awareness. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1079–1088).
  • Vyas, P., Liu, J., & El-Gayar, O. (2021). Fake news detection on the web: An LSTM-based approach. AMCIS 2021 proceedings, 5.
  • Weiss, S. M., Indurkhya, N., & Zhang, T. (2015). Fundamentals of predictive text mining. Springer.
  • Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2), 1–36. https://doi.org/10.1145/3161603

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.