Examining Hashtag Use of #blackboyjoy and #theblackmancan and Related Content on Instagram: Descriptive Content Analysis
Bibliographic record
Abstract
BACKGROUND: Social media is widely accessible and increasingly utilized. Social media users develop hashtags and visual, text-based imagery to challenge misrepresentations, garner social support, and discuss a variety of mental health issues. Understanding how Black men are represented on social media and are using social media may be an avenue for promoting their engagement with and uptake of digital mental health interventions. OBJECTIVE: The aim of this study was to conduct a content analysis of posts containing visual and text-based components related to representations of Black men's race, gender, and behaviors. METHODS: An exploratory, descriptive content analysis was conducted for 500 Instagram posts to examine characteristics, content, and public engagement of posts containing the hashtags #theblackmancan and #blackboyjoy. Posts were selected randomly and extracted from Instagram using a social network mining tool during Fall 2018 and Spring 2019. A codebook was developed, and all posts were analyzed by 2 independent coders. Analyses included frequency counts and descriptive analysis to determine content and characteristics of posts. Mann-Whitney U tests and Kruskal-Wallis H tests were conducted to assess engagement associated with posts via likes, comments, and video views. RESULTS: Of the 500 posts extracted, most were image based (368/500, 73.6%), 272/500 (54.4%) were posted by an individual and 135/500 (27.0%) by a community organization, 269/500 (53.8%) were posted by individuals from Black populations, and 177/500 (35.4%) posts contained images of only males. Posts depicted images of Black men as fathers (100/500, 20.0%), Black men being celebrated (101/500, 20.2%), and Black men expressing joy (217/500, 43.4%). Posts (127/500, 25.4%) also depicted Black men in relation to gender atypical behavior, such as caring for children or styling their children's hair. Variables related to education and restrictive affection did not show up often in posts. Engagement via likes (median 1671, P<.001), comments (P<.001), and views (P<.001) for posts containing #theblackmancan was significantly higher compared with posts containing #blackboyjoy (median 140). Posts containing elements of celebrating Black men (P=.02) and gender atypical behavior (P<.001) also had significantly higher engagement. CONCLUSIONS: This is one of the first studies to look at hashtag use of #blackboyjoy and #theblackmancan. Posts containing #blackboyjoy and #theblackmancan promoted positive user-generated visual and text-based content on Instagram and promoted positive interactions among Black and diverse communities. With the popularity of social media and hashtag use increasing, researchers and future interventional research should investigate the potential for such imagery to serve as culturally relevant design components for digital mental health prevention efforts geared towards Black men and the communities they exist and engage with.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".