Misinformation; Social Networks; Emotions; Topics; Echo Chambers
Abstract :
[en] Misinformation has become one of the most pressing social issues in the twenty-first century. How the combinations of emotions and topics trigger the spread of misinformation, however, still remains to be revealed. This study comprehensively examines misinformation and its diffusion by correlating emotions and topics. First, we examine how specific emotions and topics are combined in misinformation. Second, we identify the effects of emotions and topics on the virality of misinformation. Finally, we further explore how to employ users’ topic preferences and emotion reactions to detect and analyze echo chambers in misinformation cascades. The findings can help construct a detailed and consistent understanding on misinformation diffusion in terms of emotions and topics. Potential practical implications are also provided to prevent the spread of misinformation online.
Disciplines :
Computer science
Author, co-author :
CHUAI, Yuwei ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
ROSSI, Arianna ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
LENZINI, Gabriele ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
External co-authors :
no
Language :
English
Title :
Using Emotions and Topics to Understand Online Misinformation
Publication date :
16 June 2023
Event name :
23rd International Conference on Web Engineering
Event date :
from 6-6-2023 to 9-6-2023
Main work title :
Web Engineering. ICWE 2023. Lecture Notes in Computer Science, vol 13893. Springer, Cham.
Charquero-Ballester, M., Walter, J.G., Nissen, I.A., Bechmann, A.: Different types of COVID-19 misinformation have different emotional valence on Twitter. Big Data Soc. 8(2), 20539517211041280 (2021)
Chuai, Y., Chang, Y., Zhao, J.: What really drives the spread of COVID-19 Tweets: a revisit from perspective of content. In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-10. IEEE (2022)
Chuai, Y., Zhao, J.: Anger can make fake news viral online. Front. Phys. 10, 970174 (2022)
Diaz-Diaz, F., San Miguel, M., Meloni, S.: Echo chambers and information trans¬mission biases in homophilic and heterophilic networks. Sci. Rep. 12(1), 9350 (2022)
Ecker, U.K., et al.: The psychological drivers of misinformation belief and its resis¬tance to correction. Nat. Rev. Psychol. 1(1), 13-29 (2022)
Falkenberg, M., et al.: Growing polarization around climate change on social media. Nat. Clim. Change 12, 1114-1121 (2022)
Glockner, M., Hou, Y., Gurevych, I.: Missing counter-evidence renders NLP fact-checking unrealistic for misinformation. arXiv (2022)
Gradon, K.T., Hoiyst, J.A., Moy, W.R., Sienkiewicz, J., Suchecki, K.: Coun¬tering misinformation: a multidisciplinary approach. Big Data Soc. 8(1), 20539517211013850 (2021)
Hartmann, J.: Emotion english distilroberta-base. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/(2022)
Lazer, D.M., et al.: The science of fake news. Science 359(6380), 1094-1096 (2018)
Prollochs, N., Bar, D., Feuerriegel, S.: Emotions in online rumor diffusion. EPJ Data Sci. 10(1), 1-17 (2021). https://doi.org/10.1140/epjds/s13688-021-00307-5
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019)
Ross Arguedas, A., Robertson, C., Fletcher, R., Nielsen, R.: Echo chambers, filter bubbles, and polarisation: a literature review. Reuters Institute for the Study of Journalism (2022)
Sehgal, V., Peshin, A., Afroz, S., Farid, H.: Mutual hyperlinking among misinfor¬mation peddlers. arXiv (2021)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359, 1146-1151 (2018)
Wu, L., Morstatter, F., Carley, K.M., Liu, H.: Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explor. Newsl. 21(2), 80-90 (2019)
