Combining Crowd and Machine Intelligence to Detect False News on Social Media

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Publication History

Received: May 10, 2019
Revised: February 1, 2020; April 11, 2021
Accepted: June 21, 2021
Published Online as Accepted Author Version: February 22, 2022
Published Online as Articles in Advance: May 25, 2022
Published in Issue: June 1, 2022

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The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection.

Additional Details
Author Xuan Wei, Zhu Zhang, Mingyue Zhang, Weiyun Chen, and Daniel Dajun Zeng
Year 2022
Volume 46
Issue 2
Keywords false news, fake news, wisdom of crowds, hybrid intelligence, graphical model
Page Numbers 977-1008
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