Algorithmic Surveillance Anxiety Index (ASAI) Dataset: YouTube Audience Discourse on Algorithmic Surveillance and E-Eavesdropping — Fear vs Acceptance Corpus (n=2,527, April 2026)

This dataset contains 2,527 YouTube comments collected viathe YouTube Data API v3 (April 2026) across fivesurveillance discourse domains targeting naturalisticconsumer responses to algorithmic surveillance and thee-eavesdropping belief — the perception that electronicdevices listen to offline conversations for commercialpurposes (Segijn et al., 2025): (1) my phone is listening to me targeted ads proof 2025(2) algorithm knows what I am thinking scary surveillance(3) social media algorithm listening microphone conspiracy(4) why does my phone show ads after I talk about something(5) big tech surveillance algorithm tracking everything 2025 The corpus is the primary empirical dataset for theAlgorithmic Surveillance Anxiety Index (ASAI) — a novelcomputational metric measuring the ratio of SurveillanceFear to Algorithmic Acceptance in naturalistic consumerdiscourse about algorithmic surveillance. ASAI = Surveillance Fear / (Surveillance Fear + Algorithmic Acceptance)1.0 = pure Surveillance Fear (maximum anxiety)0.5 = balanced fear and acceptance0.0 = pure Algorithmic Acceptance (normalisation) Three signal types — distinct from all prior metrics: SURVEILLANCE FEAR (SF): visceral violation emotion — "it knows", "it's listening", "spying", "no privacy" Distinct from: AAI (general AI anxiety), CAABI (deception anger), ATBI (governance trust) ALGORITHMIC ACCEPTANCE (AA): normalisation of surveillance as convenience trade-off — "just data", "useful", "I don't mind", "nothing to hide" ALGORITHMIC HELPLESSNESS (AH): epistemic fatalism — "nothing we can do", "no escape", "already happened" Grounded in Seligman (1972) learned helplessness theory Grounded in:- Segijn et al. (2025) e-eavesdropping surveillance beliefs- Zuboff (2019) surveillance capitalism- Westin (1967) privacy as autonomy- Trepte (2021) privacy as social contract- Seligman (1972) learned helplessness theory KEY FINDINGS:- Total corpus: n=2,527 comments, ~50 videos- Categorised (ASAI computed): 165 (6.5%)- Surveillance Fear cluster: 114 (69.1% of categorised)- Algorithmic Acceptance: 36 (21.8%)- Algorithmic Helplessness: 10 (6.1%)- Contested: 5 (3.0%) DOMAIN-LEVEL ASAI — THE KEY THEORETICAL FINDING:- Social media microphone conspiracy: ASAI=0.9054 (max fear) SF=5.98/100 | AA=1.09/100 — near-pure fear dominant- Phone listening / targeted ads: ASAI=0.7778 SF=4.35/100 | AA=1.11/100 — fear dominant- Algorithm knows my thoughts: ASAI=0.7321 SF=7.61/100 | AA=1.56/100 — fear dominant, highest SF density- Big tech tracking: ASAI=0.1429 SF=2.86/100 | AA=4.39/100 — ACCEPTANCE dominant- Why phone shows ads after talking: ASAI=0.0000 SF=0.00/100 | AA=10.58/100 — PURE ACCEPTANCE, zero fear CRITICAL INVERSION FINDING:When consumers frame surveillance as "why does my phoneshow ads" (functional/curious framing) → ASAI=0.0000,pure Algorithmic Acceptance. When framed as "conspiracy/microphone" → ASAI=0.9054. Same phenomenon, oppositeemotional responses depending on discourse framing.This is the first computational evidence that framingdetermines surveillance anxiety — not surveillance itself. Files:- asai_videos.csv: ~50 unique videos metadata- asai_comments.csv: 2,527 raw comments- asai_results.csv: ASAI scores + signal annotation Method: YouTube Data API v3. Python 3.12, April 2026.