Bibliographic record
Abstract
Powered by the Internet and web technology, online forums provide users with a virtual space to connect, communicate, and share information between diverse individuals. However, the premise on information accuracy can be challenged as not all information on the online forum is created equally reliable and representative. We focus on a unique setting—online sports betting—where gamblers’ betting decisions are directly linked to financial outcomes and heavily influenced by game information and other gamblers’ opinions. We study how information from online forums affect bettors’ risk preferences and financial returns. We find that more information does not necessarily lead to better financial outcomes for gamblers in online sports betting. For every additional post a bettor reads, their net return decreases by $1.13. Further analyses of the underlying mechanism reveal that the information shared on online forums is biased toward high-risk events–the betting odds shared on forum posts are often much higher than the overall betting odds and therefore are not a fair characterization of how other bettors evaluate gambling risks. Trusting this biased information can potentially skew information seekers’ perceptions and encourage them to bet on events with a lower likelihood of success. This bias likely stems from the fact that extreme bets attract more attention, motivating forum contributors to post wagers with lower chances of winning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".