AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Curbing the dissemination of fake news in social media has been a major issue in recent years. Previous studies have suggested that the general public can recognize fake news, showing the feasibility of applying crowd ratings to identify fake news. However, the effectiveness of crowd ratings for curbing the dissemination of fake news is uncertain. This study constructed an online experimental platform to simulate Sina Microblog and designed a crowd rating strategy to compare and validate the difference between the absence vs. the presence of crowd ratings, and crowd ratings vs. expert ratings, in curbing the dissemination of fake news. We found that the presence of crowd ratings inhibited users’ dissemination of fake news compared to the absence of crowd ratings. Moreover, there was no significant difference between the suppression effects of crowd ratings and expert ratings, both of which were effective in curbing the dissemination of fake news. Crowd rating uses collective intelligence to intervene in users’ perceptions and behaviors at the onset of fake news dissemination, providing a cost-effective and efficient solution to combat the spread of fake news on social media.
F. Saurwein and C. Spencer-Smith, Combating disinformation on social media: Multilevel governance and distributed accountability in Europe, Digit. Journalism, vol. 8, no. 6, pp. 820–841, 2020.
Y. Wu, E. W. T. Ngai, P. Wu, and C. Wu, Fake online reviews: Literature review, synthesis, and directions for future research, Decis. Support. Syst., vol. 132, p. 113280, 2020.
J. Y. Cuan-Baltazar, M. J. Muñoz-Perez, C. Robledo-Vega, M. F. Pérez-Zepeda, and E. Soto-Vega, Misinformation of COVID-19 on the Internet: Infodemiology study, JMIR Public Health Surveillance, vol. 6, no. 2, p. e18444, 2020.
B. Collins, D. T. Hoang, N. T. Nguyen, and D. Hwang, Trends in combating fake news on social media—A survey, J. Inf. Telecommun., vol. 5, no. 2, pp. 247–266, 2021.
D. Gavric and A. Bagdasaryan, A fuzzy model for combating misinformation in social network twitter, J. Phys.: Conf. Ser., vol. 1391, no. 1, p. 012050, 2019.
J. Zhu, P. Ni, and G. Wang, Activity minimization of misinformation influence in online social networks, IEEE Trans. Comput. Soc. Syst., vol. 7, no. 4, pp. 897–906, 2020.
D. Geiger and M. Schader, Personalized task recommendation in crowdsourcing information systems—Current state of the art, Decis. Support. Syst., vol. 65, pp. 3–16, 2014.
L. V. S. Lakshmanan, S. Michael, and T. Saravanan, Combating fake news: A data management and mining perspective, Proc. VLDB Endow., vol. 12, no. 12, pp. 1990–1993, 2019.
G. Pennycook and D. G. Rand, Fighting misinformation on social media using crowdsourced judgments of news source quality, Proc. Natl. Acad. Sci. U.S.A., vol. 116, no. 7, pp. 2521–2526, 2019.
M. Coscia and L. Rossi, Distortions of political bias in crowdsourced misinformation flagging, J. R. Soc. Interface., vol. 17, no. 167, p. 20200020, 2020.
M. Cheng, C. Yin, S. Nazarian, and P. Bogdan, Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena, Sci. Rep., vol. 11, p. 10424, 2021.
P. L. Moravec, A. Kim, and A. R. Dennis, Appealing to sense and sensibility: System 1 and system 2 interventions for fake news on social media, Inf. Syst. Res., vol. 31, no. 3, pp. 987–1006, 2020.
F. Safieddine, W. Masri, and P. Pourghomi, Corporate responsibility in combating online misinformation, Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 2, pp. 126–132, 2016.
A. Bergström and M. J. Belfrage, News in social media: Incidental consumption and the role of opinion leaders, Digital Journalism, vol. 6, no. 5, pp. 583–598, 2018.
S. Vosoughi, D. Roy, and S. Aral, The spread of true and false news online, Science, vol. 359, no. 6380, pp. 1146–1151, 2018.
T. Buchanan, Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation, PLoS One, vol. 15, no. 10, p. e0239666, 2020.
B. Swire, A. J. Berinsky, S. Lewandowsky, and U. K. H. Ecker, Processing political misinformation: Comprehending the Trump phenomenon, R. Soc. Open Sci., vol. 4, no. 3, p. 160802, 2017.
H. Li and Y. Sakamoto, Social impacts in social media: An examination of perceived truthfulness and sharing of information, Comput. Hum. Behav., vol. 41, pp. 278–287, 2014.
A. Kim, P. L. Moravec, and A. R. Dennis, Combating fake news on social media with source ratings: The effects of user and expert reputation ratings, J. Manag. Inf. Syst., vol. 36, no. 3, pp. 931–968, 2019.
B. D. Horne, D. Nevo, S. Adali, L. Manikonda, and C. Arrington, Tailoring heuristics and timing AI interventions for supporting news veracity assessments, Comput. Hum. Behav. Rep., vol. 2, p. 100043, 2020.
G. Pennycook, Z. Epstein, M. Mosleh, A. A. Arechar, D. Eckles, and D. G. Rand, Shifting attention to accuracy can reduce misinformation online, Nature, vol. 592, no. 7855, pp. 590–595, 2021.
M. Soprano, K. Roitero, D. La Barbera, D. Ceolin, D. Spina, S. Mizzaro, and G. Demartini, The many dimensions of truthfulness: Crowdsourcing misinformation assessments on a multidimensional scale, Inf. Process. Manag., vol. 58, no. 6, p. 102710, 2021.
B. Bago and W. De Neys, Fast logic? Examining the time course assumption of dual process theory, Cognition, vol. 158, pp. 90–109, 2017.
A. R. Dennis and R. K. Minas, Security on autopilot: Why current security theories hijack our thinking and lead us astray, The DATA BASE for Advances in Information Systems, vol. 49, no. SI, pp. 15–38, 2018.
E. Aronson, The theory of cognitive dissonance: A current perspective, Advances in Experimental Social Psychology, vol. 4, pp. 1–34, 1969.
D. Kahneman, Maps of bounded rationality: Psychology for behavioral economics, Am. Econ. Rev., vol. 93, no. 5, pp. 1449–1475, 2003.
C. Kim and S. U. Yang, Like, comment, and share on Facebook: How each behavior differs from the other, Public Relat. Rev., vol. 43, no. 2, pp. 441–449, 2017.
A. W. Woolley and E. Fuchs, PERSPECTIVE—Collective intelligence in the organization of science, Organ. Sci., vol. 22, no. 5, pp. 1359–1367, 2011.
A. W. Woolley, R. M. Chow, A. T. Mayo, C. Riedl, and J. W. Chang, Collective attention and collective intelligence: The role of hierarchy and team gender composition, Organ. Sci., vol. 34, no. 3, pp. 1315–1331, 2023.
A. W. Woolley, C. F. Chabris, A. Pentland, N. Hashmi, and T. W. Malone, Evidence for a collective intelligence factor in the performance of human groups, Science, vol. 330, no. 6004, pp. 686–688, 2010.
B. Golub and M. O. Jackson, Naive learning in social networks and the wisdom of crowds, American Economic Journal: Microeconomics, vol. 2, no. 1, pp. 112–149, 2010.
A. Pérez Escoda, G. Barón-Dulce, and J. Rubio-Romero, Mapeo del consumo de medios en los jóvenes: Redes sociales, ‘fake news’ y confianza en tiempos de pandemia, Index Comun., vol. 11, no. 2, pp. 187–208, 2021.
A. M. de Vicente Domínguez, A. B. Bañares, and J. S. Sánchez, Young Spanish adults and disinformation: Do they identify and spread fake news and are they literate in it, Publications, vol. 9, no. 1, p. 2, 2021.
P. Borah, B. Irom, and Y. C. Hsu, ‘It infuriates me’: Examining young adults’ reactions to and recommendations to fight misinformation about COVID-19, J. Youth Stud., vol. 25, no. 10, pp. 1411–1431, 2022.
A. Pérez-Escoda and L. M. P. Esteban, Retos del periodismo frente a Las redes sociales, Las fake news y la desconfianza de la generación Z, Rev. Lat. De Comun. Soc., no. 79, pp. 67–85, 2021.
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
京公网安备11010802044758号