Ning Sa


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Adapting Emotion Detection to Analyze Influence Campaigns on Social Media
Ankita Bhaumik | Andy Bernhardt | Gregorios Katsios | Ning Sa | Tomek Strzalkowski
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.


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Generating Ethnographic Models from Communities’ Online Data
Tomek Strzalkowski | Anna Newheiser | Nathan Kemper | Ning Sa | Bharvee Acharya | Gregorios Katsios
Proceedings of the Second Workshop on Figurative Language Processing

In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities’ models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community’s online language convey the bias in the community’s worldview.


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Discovering Conceptual Metaphors using Source Domain Spaces
Samira Shaikh | Tomek Strzalkowski | Kit Cho | Ting Liu | George Aaron Broadwell | Laurie Feldman | Sarah Taylor | Boris Yamrom | Ching-Sheng Lin | Ning Sa | Ignacio Cases | Yuliya Peshkova | Kyle Elliot
Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)