| Title | Scalable Fact-checking with Human-in-the-Loop |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yang, Jing, Vega-Oliveros, Didier, Seibt, Tais, Rocha, Anderson |
| Conference Name | 2021 IEEE International Workshop on Information Forensics and Security (WIFS) |
| Keywords | clustering methods, Forensics, human in the loop, Inspection, Manuals, Pipelines, pubcrawl, Scalability, Semantics, social networking (online) |
| Abstract | Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline - grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages. |
| DOI | 10.1109/WIFS53200.2021.9648388 |
| Citation Key | yang_scalable_2021 |
Scalable Fact-checking with Human-in-the-Loop