The art of creating an informative data collection for automated deception detection: A corpus of truths and lies
Bibliographic record
Abstract
Abstract One of the novel research directions in Natural Language Processing and Machine Learning involves creating and developing methods for automatic discernment of deceptive messages from truthful ones. Mistaking intentionally deceptive pieces of information for authentic ones (true to the writer's beliefs) can create negative consequences, since our everyday decision‐making, actions, and mood are often impacted by information we encounter. Such research is vital today as it aims to develop tools for the automated recognition of deceptive, disingenuous or fake information (the kind intended to create false beliefs or conclusions in the reader's mind). The ultimate goal is to support truthfulness ratings that signal the trustworthiness of the retrieved information, or alert information seekers to potential deception. To proceed with this agenda, we require elicitation techniques for obtaining samples of both deceptive and truthful messages from study participants in various subject areas. A data collection, or a corpus of truths and lies, should meet certain basic criteria to allow for meaningful analysis and comparison of socio‐linguistic behaviors. In this paper we propose solutions and weigh pros and cons of various experimental set‐ups in the art of corpus building. The outcomes of three experiments demonstrate certain limitations with using online crowdsourcing for data collection of this type. Incorporating motivation in the task descriptions, and the role of visual context in creating deceptive narratives are other factors that should be addressed in future efforts to build a quality dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".