Exploring the Use of Social Media for Activism by Mexican Nongovernmental Organizations Using Posts From the 16 Days of Activism Against Gender-Based Violence Campaign: Thematic Content Analysis
Bibliographic record
Abstract
BACKGROUND: In the past decade, hashtag feminism has emerged in Mexico as a prevalent strategy to build social movements against gender-based violence (GBV). For example, during the global "16 Days of Activism Against GBV" campaign held between November 25 and December 10 each year, Mexico-based nongovernmental organizations (NGOs) turn to X (formerly known as Twitter) to share messages. Despite this prevalence, there is limited research on the type of information shared by these NGO activists on social media and the public's engagement with these messages. OBJECTIVE: This study aims to explore the themes covered by Mexican NGOs on X and examine what types of messages related to GBV potentially resonated more with the public. METHODS: We collated and reviewed posts (commonly known as tweets) published in Spanish on the platform X by Mexico-based NGOs between November 25 and December 10 of 2020, 2021, and 2022, a period when digital interactions increased during the COVID-19 pandemic. We then extracted posts using the following 4 hashtags: #16días, #16DiasdeActivismo, or #16DíasdeActivismo; #25N or #25Noviembre; #DiaNaranja or #DíaNaranja; and #PintaElMundoDeNaranja. We subsequently assessed the number of likes each post had and retained the top 200 posts from each year with the highest number of likes. We used the iterative content analysis process and the inductive 6-step qualitative thematic analysis method in NVivo software to code and analyze the final 600 posts. RESULTS: Five themes emerged from the 16 Days of Activism Against GBV campaigns, covering both knowledge-sharing and activism-generating messages as follows: (1) activism and how to be an activist, (2) types of GBV most commonly highlighted in posts, (3) changing public discourse surrounding GBV, (4) GBV as a violation of human rights, and (5) the COVID-19 pandemic's impact on GBV. Most of the messages on these posts exclusively mentioned women and younger girls, while a few included adolescents. Gaps in the representation of vulnerable populations were also found. CONCLUSIONS: The posts from this campaign that were highly liked by the public reflect some of the most significant societal issues currently present in the country. Our results could help guide further GBV campaigns. Still, further research related to hashtag feminism by Mexico-based NGOs on GBV is needed to understand the population that NGOs reach and how the messages shared on these campaigns translate into activism on online and offline social media platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".