The Fake News Spreading Plague: Was it Preventable?
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Abstract (arXiv)
In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Mar 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
- 2023It's All in the Embedding! Fake News Detection Using Document Embeddingscited by 7
- 2022Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Datasetcited by 4
- 2023Fake News Detection and Behavioral Analysis: Case of COVID-19cited by 0
- 2026BharatGather: A Culturally-Informed Benchmark Dataset for Misinformation and Fake News Detection in Indian Public Eventscited by 0
- 2026BharatGather: A Culturally-Informed Benchmark Dataset for Misinformation and Fake News Detection in Indian Public Events2 Jul 2026
- 2023Fake News Detection and Behavioral Analysis: Case of COVID-1925 May 2023
- 2023It's All in the Embedding! Fake News Detection Using Document Embeddings16 Apr 2023
- 2022Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset6 Apr 2022
- 2021Dataset of Fake News Detection and Fact Verification: A Survey5 Nov 2021