Papers › A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Benjamin Riedel, Isabelle Augenstein, Georgios P. Spithourakis, Sebastian Riedel
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fake News Detection | FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Per-class Accuracy (Agree) | 44.04 | #5 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Per-class Accuracy (Disagree) | 6.60 | #5 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Per-class Accuracy (Discuss) | 81.38 | #5 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Per-class Accuracy (Unrelated) | 97.90 | #5 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Weighted Accuracy | 81.72 | #5 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections