Purpose of the Report
This report was prepared with contributions from the Iraqi Network for social media (INSM) and
examines the main gendered narratives and TFGBV targeting women candidates during the 2025 Iraqi
parliamentary elections.
The report aims to:
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Examine public discourse on social media surrounding women candidates during different
stages of the electoral cycle, including the pre-election nomination period, campaigning
period, polling day, and post-election.
Track the digital presence of women candidates across social media platforms in Iraq,
documenting how they are represented, discussed, and targeted in online public spaces.
Analyse patterns of TFGBV and hate speech directed at women candidates, identifying the
tactics, techniques, and narrative framing of these attacks.
Identify the dominant narratives used to undermine women’s political participation,
including moral shaming, defamation, misogynistic harassment and abuse, sexualized
disinformation, and delegitimization of women’s leadership.
Provide evidence-based national and regional recommendations to address misogynistic and
gendered hate speech and online gender-based violence, and to strengthen protections for
women’s political participation in digital spaces.
Methodology and Data Approaches
This report adopts a mixed-methods approach combining qualitative digital monitoring and
quantitative analysis of social media comments related to the 2025 Iraqi parliamentary elections.
Monitoring focused on the three technology platforms that shape digital political conversations in Iraq
and the KRI: Facebook, X, and TikTok.
The monitoring period covered October 2025 to January 2026, allowing the analysis to capture online
discourse through the electoral cycle. Previous work by INSM saw the creation of an AI-based social
media monitoring technology tailored to the Iraqi context that aimed to generate evidence on the
prevalence of TFGBV particularly during key democratic events, such as elections. The project
developed a machine learning model in Arabic and Kurdish to detect online gender hate speech in
Iraqi and Kurdish language which classified textual comments on social media network posts. The
model classification followed different categories of TFGBV drawing on international norms and
language. The current Safa project applied this approach and methodology to gather data and analysis
from across the period of the Iraqi elections in 2025.
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