Instagram Posts Promoting Colorectal Cancer Awareness: Content Analysis of Themes and Engagement During Colorectal Cancer Awareness Month
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, with early detection and screening being critical for reducing mortality. Social media platforms like Instagram offer a unique opportunity to raise awareness about CRC, particularly during designated awareness months. However, there is limited research on the effectiveness of CRC-related content on Instagram. Objective: This study aims to examine how Instagram is used to raise awareness about CRC during Colorectal Cancer Awareness Month by analyzing the thematic content and engagement metrics of related posts. The research seeks to identify the prevalent themes, assess audience interaction with these messages, and highlight areas for improvement in leveraging Instagram as a tool for cancer awareness campaigns. Methods: A total of 150 Instagram posts were collected based on their use of specific hashtags related to CRC awareness (#colorectalcancer, #colorectalcancerawareness, #colorectalcancerawarenessmonth) during March 2024. The text and images in the posts were categorized into themes such as screening and early detection, symptoms, general awareness, risk factors, individual's experiences, representation of racial and ethnic minoritized communities, and representation of women. Engagement metrics, including the number of likes and comments, were also analyzed. Two researchers independently coded the posts, achieving high interrater reliability (Cohen κ=0.93). Results: Organizational accounts were more active, contributing 82% (n=123) of the 150 posts, compared to 18% (n=27) from individual users. The most frequently mentioned theme was screening and early detection, which made up 37.3% (n=56) of all posts. General awareness came in second at 19.3% (n=29), and risk factors came in third at 12% (n=18). Posts about individual experiences and general awareness received the highest engagement, indicating the effectiveness of personal narratives and broad informational content. Themes related to symptoms and representation of racial and ethnic minoritized communities and women were underrepresented. Conclusions: This study highlights the potential of Instagram as a platform for promoting CRC awareness, particularly through posts about screening and early detection and personal experiences. However, there is a need for more inclusive and diverse content to ensure a broader reach and impact.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".