flan-large-lora-stance-human7

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Model was trained and used for the following paper A Theory-Driven Approach to Fake News/Information Disorder Analysis and Explanation via Target-Based Emotion–Stance Analysis (TESA) and Interpretive Graph Generation (IGG) https://journals.sagepub.com/doi/abs/10.1177/08944393251338403

The average model performance across different 5 test sets were weighted F1 0.694 for emotion, weighted F1 0.774 for stance

Two datasets were used for this study:

• Fakenews KDD 2020 (Subho, 2020) features labels indicating whether news articles were categorized as fake or real. This dataset was utilized in a competition hosted as part of the Second International TrueFact Workshop, held in conjunction with SIGKDD 2020. • The CLEF-2022 dataset (Köhler et al., 2022) drew data from 20 different websites, with AFP among the sources. To streamline the analysis, the four categories: false, partially false, true, or other were merged into real and fake (consolidating, false, partially false, and other).

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