
arXiv: 2402.13244
Fact-checking-specific search tools such as Google Fact Check are a promising way to combat misinformation on social media, especially during events bringing significant social influence, such as the COVID-19 pandemic and the U.S. presidential elections. However, the usability of such an approach has not been thoroughly studied. We evaluated the performance of Google Fact Check by analyzing the retrieved fact-checking results regarding 1,000 COVID-19-related false claims and found it able to retrieve the fact-checking results for 15.8% of the input claims, and the rendered results are relatively reliable. We also found that the false claims receiving different fact-checking verdicts (i.e., "False," "Partly False," "True," and "Unratable") tend to reflect diverse emotional tones, and fact-checking sources tend to check the claims in different lengths and using dictionary words to various extents. Claim variations addressing the same issue yet described differently are likely to retrieve distinct fact-checking results. We suggest that the quantities of the retrieved fact-checking results could be optimized and that slightly adjusting input wording may be the best practice for users to retrieve more useful information. This study aims to contribute to the understanding of state-of-the-art fact-checking tools and information integrity.
Accepted and presented at the 5th EAI International Conference on Data and Information in Online Environments (EAI DIONE 2024)
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Social and Information Networks
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Social and Information Networks
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
