Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States (2021.findings-emnlp)
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| Challenge: | Existing studies on political responsiveness focus on long-term policies collected over decades . recent COVID-19 pandemic has given rise to a new political phenomenon, where political leaders make frequent short-term decisions on the same controlled topic. |
| Approach: | They propose to use Twitter data to classify the sentiments toward governors of each state and conduct controlled studies and comparisons. |
| Outcome: | The proposed model focuses on the COVID-19 pandemic, where political leaders make frequent short-term decisions on the same controlled topic. |
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| Challenge: | Social distancing orders are the most effective strategy to reduce the spread of COVID-19 in 2020 . |
| Approach: | They propose to use NLP methods in a causal mediation scenario to emphasize the use of NLP and economics to decouple the effect of government restrictions on mobility from the effect that occurs due to public perception of the COVID-19 strategy. |
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Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
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Classification without (Proper) Representation: Political Heterogeneity in Social Media and Its Implications for Classification and Behavioral Analysis (2022.findings-acl)
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| Challenge: | Prior work has shown that partisan leanings can be inferred from a diverse set of behavioral characteristics such as text, social networks, and even community participation. |
| Approach: | They test this assumption and show that commonly-used models do not generalize . they also show that political users are more toxic on the platform and inter-party interactions are even more toxic . |
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Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)
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| Challenge: | a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language. |
| Approach: | They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings . |
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Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media (2024.lrec-main)
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| Challenge: | a social media analysis of online influence campaigns can reveal the sources of agenda setting . annotated data is limited or nonexistent, but there are methods to detect agenda control . |
| Approach: | They propose a method for detecting instances of agenda control through social media . they use a modest corpus of tweets centered on the 2022 french presidential election . |
| Outcome: | The proposed method overcomes the requirement for large annotated training dataset. |
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022.emnlp-main)
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| Challenge: | Large-scale crises such as the COVID-19 pandemic cause emotional turmoil worldwide. |
| Approach: | They propose a method to jointly detect emotions and summarize emotion triggers in social media posts related to COVID-19. |
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Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (2022.acl-long)
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| Challenge: | State-level legislation is the cornerstone of national policies and has long-lasting effects on residents of US states. |
| Approach: | They build a dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. |
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ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19 (2021.emnlp-demo)
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| Challenge: | a new machine reading system ingests open-source text documents to analyze COVID-19 events . the system extracts COVId-19 related events and relations between them . |
| Approach: | They propose a machine reading system that ingests open-source text documents and extracts COVID-19 related events and relations between them. |
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Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
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| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
| Approach: | This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities. |
| Outcome: | The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area. |
Emotion analysis and detection during COVID-19 (2022.lrec-1)
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| Challenge: | 3,000 English tweets labeled with emotions are used to predict emotions during crises . authors propose semi-supervised learning to bridge this gap . |
| Approach: | They propose to use a dataset of 3,000 English tweets labeled with emotions . they propose semi-supervised learning to bridge this gap by analyzing unlabeled data . |
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