Identifying COVID-19 Instagram behaviour patterns via a novel network analysis pipeline
Bibliographic record
Abstract
Instagram has become one of the most widely used social media platforms globally. As such, the volume of information generated by Instagram users makes it an excellent data mine to study human behaviour. Although researchers have turned to Twitter data to observe the flow of misinformation surrounding the COVID-19 pandemic, there is little to no publication on COVID-19 behaviours on Instagram. To address this knowledge gap, an extensive analysis was conducted on COVID-19-related big data from Instagram. 5,300 global Instagram posts were collected between January 5 and March 20 of 2020, and the associated hashtags were processed using a novel, highly extensible Social Network Analysis pipeline. Eigenvector centrality and weight cluster indexing partitioned the hashtag data into eleven clusters, three of which were studied to reflect public behaviour. The data revealed that the mental health cluster consisted mainly of Instagram business profiles and that these were associated with positive sentiments on COVID-19. It was found that a niche cluster containing conspiracy-related content was primarily created by real Instagram users and was associated with very negative sentiments. A supervised machine learning approach was also used to classify hashtags related to comedy and memes as bot-generated content. For future investigations, it is recommended that a large-scale behavioural analysis of bots and their effects on pandemic information be performed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".