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Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.27/ Abstract: Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture "stance" as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both "Pro" and "Anti"’ stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/xqk2-6q12 https://underline.io/lecture/37842-(mis)alignment-between-stance-expressed-in-social-media-data-and-public-opinion-surveys
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Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach ...
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Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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