Ethical and Legal Issues in E-Discovery of Facebook Evidence in Civil Litigation
Bibliographic record
Abstract
The increasing interest by litigating counsel in discovery of social media evidence in civil litigation comes with ethical and legal challenges. The ethical challenges arise from the duty of counsel to investigate facts, and preserve evidence related to litigation, as well as the ethical rule prohibiting contact with a represented person including contact by means of social media. There are also legal challenges associated with the process for discovering, preserving and collecting relevant evidence in social media in the course of litigation. This paper examines the ethical and legal issues in social media e-discovery in the course of civil litigation with focus on personal injury litigation. The paper begins with a general overview of Facebook as a social media platform, then it proceeds to examine the ethical issues involved in social media e-discovery by counsel in the light of the Federation of Law Societies of Canada’s Model Code of Professional Conduct. The paper concludes with examination of how case law across selected jurisdictions in Canada has sought to address the legal issues arising from e-discovery of Facebook evidence in civil litigation. While this paper focuses on Facebook (the most popular social media platform), the issues raised and discussed also apply to other social media platforms.
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 | 0.064 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.052 |
| Scholarly communication | 0.034 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.021 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".