Knowing Your Users by Heart: A Critical Examination of the Scientific Research on Emotions Conducted by Social Media Platforms
Bibliographic record
Abstract
Since their inception, social media platforms have continuously developed innovations and discourses centered on affective and emotional dimensions. Platform owners invest heavily in research and development to both identify and elicit feelings and emotions. However, the research conducted by these companies on this topic remains understudied. In this study, we present a systematic and critical analysis of the scientific literature published by the research teams of Meta (Facebook, Instagram) and Google (YouTube) regarding the analysis and processing of their users’ emotions. We first demonstrate that this literature relies on schematic definitions of emotions, thereby simplifying the complexity of affective phenomena for facilitating their automated detection. We then highlight the objectives pursued by these research efforts, the majority of which aim to promote users’ well-being through the instrumentalization of what affects them. Finally, we examine the various methods employed to identify emotions, which primarily serve to feed these platforms’ machine learning algorithms with data labeled as emotional. Beyond their scientific contributions, we consider these studies as narratives targeted at the scientific community and investors, serving to legitimize and support the development of specific innovations. Analyzing these works enables us to examine how the techniques, methods, and applications developed by these two companies commodify emotions and actively shape users’ online behaviors.
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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 | 0.055 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".