arXiv subjects

Adem Chanie Ali

Publications and source records attributed to Adem Chanie Ali.

4 recordsLinked to original sources

arXiv2026

SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labeled with the presence of polarization, polarization type, and polarization manifestation. Participants were asked to predict labels in three sub-tasks: (1) detecting the presence of polarization, (2) identifying the type of polarization, and (3) recognizing the polarization manifestation. The three tasks attracted over 1,000 participants worldwide and more than 10k submission on Codabench. We received final submissions from 67 teams and 73 system description papers. We report the baseline results and analyze the performance of the best-performing systems, highlighting the most common approaches and the most effective methods across different subtasks and languages. The dataset of this task is publicly available.

cs.CL↗

arXiv2026

POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization

Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. The results show that, while most models perform well in binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and demonstrate the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.

cs.CL↗

arXiv2025

Silenced voices: social media polarization and women's marginalization in peacebuilding during the Northern Ethiopia War

This study examines the complex relationship between social media, polarization, and conflict, with a focus on digital peacebuilding and women's participation, using the Northern Ethiopia War as a case study. Using a qualitative exploratory design through in-depth interviews, focus groups, and document analysis, the research examines how social media platforms influence conflict dynamics. The study applies and advances social identity, liberal feminist, and intersectionality theories to analyze social media's role in shaping conflict, mobilizing ethnic politics, and influencing women's involvement in peacebuilding. Findings reveal that the weaponization of social media intensifies polarization and offline violence. Women are disproportionately impacted through displacement, exclusion from peace negotiations, and heightened risks of gender-based violence, including rape. Contributing factors include hostile online environments, the digital divide, and prevailing socio-cultural norms. The study identifies significant gaps in leveraging digital platforms for sustainable peace, including government-imposed internet shutdowns, unregulated social media environments, and low media literacy. It recommends media literacy initiatives, inclusive peacebuilding frameworks, open and safe digital spaces, and gender-sensitive technological approaches. By centering digital technology, conflict, and gender in the Global South, this research contributes valuable insights to ongoing debates on ICT in conflict, peacebuilding, and women's empowerment.

cs.CY↗

arXiv2024

Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse

The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark dataset for Amharic, comprising 8,258 tweets annotated for three distinct tasks: category classification, identification of hate targets, and rating offensiveness and hatefulness intensities. Our study highlights that a considerable majority of tweets belong to the less offensive and less hate intensity levels, underscoring the need for early interventions by stakeholders. The prevalence of ethnic and political hatred targets, with significant overlaps in our dataset, emphasizes the complex relationships within Ethiopia's sociopolitical landscape. We build classification and regression models and investigate the efficacy of models in handling these tasks. Our results reveal that hate and offensive speech can not be addressed by a simplistic binary classification, instead manifesting as variables across a continuous range of values. The Afro-XLMR-large model exhibits the best performances achieving F1-scores of 75.30%, 70.59%, and 29.42% for the category, target, and regression tasks, respectively. The 80.22% correlation coefficient of the Afro-XLMR-large model indicates strong alignments.

cs.CL↗