Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters (2020.lrec-1)
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| Challenge: | A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent. |
| Approach: | They propose to annotate tweets containing political party names by using oracle filters to achieve lower MAEs. |
| Outcome: | The proposed method achieves a mean absolute error (MAE) of 2.71% for 2012 and 2.02% for 2012, and 2.89% for 2015 for the Dutch elections. |
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Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
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| Challenge: | Large language models such as ChatGPT exhibit striking political biases . a recent study shows that chatbots exhibit progressive, liberal, and proenvironmental biase . |
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| Challenge: | This paper examines the ability of LLMs to correctly label simple inferences with partisan conclusions. |
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