Papers with social sciences
Measuring and Modeling Language Change (N19-5)
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| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
| Approach: | This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data . |
| Outcome: | The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars. |
Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)
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| Challenge: | We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions . |
| Approach: | They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. |
| Outcome: | The proposed model generates an acceptance or rejection email based on the applicant's first name . |
A Query-Driven Topic Model (2021.findings-acl)
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| Challenge: | Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. |
| Approach: | They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics. |
| Outcome: | The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics. |
ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data (L18-1)
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Andreas Niekler, Arnim Bleier, Christian Kahmann, Lisa Posch, Gregor Wiedemann, Kenan Erdogan, Gerhard Heyer, Markus Strohmaier
| Challenge: | iLCM project develops integrated research environment for qualitative data analysis . text mining and text mining tools are extended by "Open Research Computing" |
| Approach: | iLCM project develops integrated research environment for analysis of structured and unstructured data in a "Software as a Service" architecture. |
| Outcome: | iLCM project develops integrated research environment for analysis of structured and unstructured data in a "Software as a Service" architecture. |
Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences (2024.lrec-main)
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| Challenge: | scholarly databases fail to aggregate, compare, contrast, and contextualize existing studies in service to a targeted research question. |
| Approach: | They propose to use large language models to discern evidence in support or refute of specific hypotheses based on abstracts. |
| Outcome: | The proposed method outperforms state-of-the-art methods and highlights opportunities for future research. |
Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)
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| Challenge: | Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues. |
| Approach: | They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset. |
| Outcome: | The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets. |
Hong Kong: Longitudinal and Synchronic Characterisations of Protest News between 1998 and 2020 (2022.lrec-1)
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| Challenge: | This paper examines the utility and timeliness of the Hong Kong Protest News Dataset . it sheds light on whether depth and/or manner of reporting changed over time . |
| Approach: | They use the Hong Kong Protest News Dataset to investigate synchronic news characterisations of protests in Hong Kong between 1998 and 2020. |
| Outcome: | The dataset sheds light on whether depth and/or manner of reporting changed over time, and if so, in what ways, or in response to what. |
Conceptualizing Treatment Leakage in Text-based Causal Inference (2022.naacl-main)
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| Challenge: | Existing methods to control for text-based confounders rely on assumption that there is no treatment leakage . prior literature has assumed that documents only contain information about confounder, but not about treatment assignment. |
| Approach: | They define the treatment leakage problem and propose methods to mitigate it . they remove treatment-related signal from text in a pre-processing step . |
| Outcome: | The proposed method can mitigate the problem of treatment leakage by removing the treatment-related signal from the text. |
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA (2021.emnlp-main)
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| Challenge: | Taking the exam closed book, but having read the textbook, yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misundersttoo the questions). |
| Approach: | They propose to use pre-trained language models to answer questions from introductory college textbooks and hundreds of true/false statements based on review questions written by the authors. |
| Outcome: | The proposed task includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History). |
The Role of Pragmatic and Discourse Context in Determining Argument Impact (D19-1)
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| Challenge: | Recent work shows that attributes of both the audience and communicator constitute important cues for determining argument strength. |
| Approach: | They propose to use a dataset to study the pragmatic and discourse context of argumentative claims to build predictive models that incorporate the pragmatic context of the argument. |
| Outcome: | The proposed models outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument. |
BnMMLU: Measuring Massive Multitask Language Understanding in Bengali (2026.findings-acl)
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| Challenge: | Large-scale multitask benchmarks have driven rapid progress in language modeling, yet most emphasize low-resource languages like English. |
| Approach: | They propose a benchmark for massive multitask language understanding in Bengali . they use a dataset that preserves mathematical content via MathML and a subset of questions most frequently missed by top systems to stress difficult cases. |
| Outcome: | The proposed benchmark covers 24 model variants across 11 LLM families. |
Discovering influential text using convolutional neural networks (2024.findings-acl)
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| Challenge: | Existing methods for estimating the effects of text on human evaluation are limited to testing a small number of pre-specified text treatments. |
| Approach: | They propose a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. |
| Outcome: | The proposed method can detect and predict human reactions to texts under certain assumptions. |
Tab2Text - A framework for deep learning with tabular data (2024.findings-emnlp)
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| Challenge: | Tabular data is a foundational part of social sciences and is used to fit supervised learning models. |
| Approach: | They propose a technique for transforming tabular data to text data to improve deep learning models for tabular datasets. |
| Outcome: | The proposed technique improves performance of deep learning models for tabular data. |
From Script to Stage: Automating Experimental Design for Social Simulations with LLMs (2026.findings-acl)
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| Challenge: | Xu et al., 2024): multi-agent simulations based on large language models are a new paradigm for social science research . traditional experimental design relies on interdisciplinary expertise and technical barriers . Xiaoping and Xin eli argue that LLM-driven agents are unreliable for rigorous experimental design due to hallucinations and limited verifiability. |
| Approach: | They propose a framework for multi-agent experiment design based on script generation . Script Composition, Script Finalization, and Actor Generation are the core phases of the framework . |
| Outcome: | The proposed framework lowers the barrier for social science experimental design and provides scientifically grounded decision support for policy-making. |
PSE v1.0: The First Open Access Corpus of Public Service Encounters (2024.lrec-main)
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Ingrid Espinoza, Steffen Frenzel, Laurin Friedrich, Wassiliki Siskou, Steffen Eckhard, Annette Hautli-Janisz
| Challenge: | a dataset of public service encounters in germany provides a new research directive . data from the public service encounters are used to investigate bias, bureaucratic discrimination and other power-driven dynamics in the actual communication . |
| Approach: | They propose to compile a dataset of transcribed public service encounters in germany . they propose to open up the black box of direct state-citizen interaction . |
| Outcome: | The proposed dataset allows the community to open up the black box of direct state-citizen interaction. |
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings (2024.lrec-main)
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| Challenge: | The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. |
| Approach: | They propose to use a dataset of sentences manually annotated for sentiment to train a robust sentiment identifier for parliamentary proceedings. |
| Outcome: | The proposed model performs very well on languages not seen during fine-tuning and additional fine- tuning data from other languages significantly improves the target parliament’s results. |