Bot and Sockpuppet Detection

1. Hypothesis

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve detection precision, recall and user harm more than single-score, content-only or opaque automated approaches.

2. Experiment design

Design: adversarial manipulation-detection and resistance programme using retrospective case reconstruction, controlled exposure studies and live simulation focused on Bot and Sockpuppet Detection Methods: coordinated-behaviour analysis; graph analytics; campaign reconstruction; synthetic-media forensics; controlled persuasion experiments; red-team exercises; threat intelligence; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Bot and Sockpuppet Detection Independent variables: evidence availability; provenance visibility; model or rule transparency; intervention timing; human-review level; platform context; user controls Dependent variables: detection precision, recall and user harm; false-positive harm; user trust; correction or recovery time Confounders: shared accounts, accessibility tools and privacy needs; platform differences; population composition; external events; baseline trust; data availability; reviewer expertise Measures: coordination confidence; actor authenticity; narrative propagation; exposure concentration; persuasion effect; behavioural conversion; resistance; recovery time; subtopic-specific indicators for detection precision, recall and user harm; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency Success criteria: Statistically and practically meaningful improvement in the named outcomes; calibrated uncertainty; acceptable false-positive burden; no disproportionate subgroup harm; traceable evidence; contestable decisions; repeatable performance across relevant contexts. Failure conditions: No meaningful benefit; results depend on unavailable or intrusive data; false positives or chilling effects exceed benefit; performance fails under adversarial or cross-platform conditions; affected users cannot understand, contest or recover from decisions.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Bot and Sockpuppet Detection as a testable research proposition. Problem basis: Manipulation operations combine synthetic identities, targeted persuasion, coordinated amplification and deceptive media, often crossing platforms faster than conventional detection and response systems. For Bot and Sockpuppet Detection, the specific challenge is distinguishing deceptive automated or multiply controlled identities from legitimate pseudonymity and automation. Directional expectation: If supported, the proposed approach should improve detection precision, recall and user harm, reduce correction and recovery costs, and preserve legitimate expression, privacy, procedural fairness and user agency. Proposed observations: coordination confidence; actor authenticity; narrative propagation; exposure concentration; persuasion effect; behavioural conversion; resistance; recovery time; subtopic-specific indicators for detection precision, recall and user harm; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 33/100; Novelty 70/100; Strategic Importance 90/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Bot and Sockpuppet Detection warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve detection precision, recall and user harm more than single-score, content-only or opaque automated approaches.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Bot and Sockpuppet Detection. It also records: CISA Mis-, Dis-, and Malinformation resources — https://www.cisa.gov/; EU Code of Practice on Disinformation — https://digital-strategy.ec.europa.eu/; NATO StratCom COE — https://stratcomcoe.org/; UNESCO information integrity work — https://www.unesco.org/. Evidence register status: “Seeded; authoritative source register initiated; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Bot and Sockpuppet Detection. References: CISA Mis-, Dis-, and Malinformation resources — https://www.cisa.gov/; EU Code of Practice on Disinformation — https://digital-strategy.ec.europa.eu/; NATO StratCom COE — https://stratcomcoe.org/; UNESCO information integrity work — https://www.unesco.org/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.