Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)
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| Challenge: | predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor. |
| Approach: | They propose to exploit social media and natural language processing techniques to enhance air quality prediction. |
| Outcome: | The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores. |
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Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
| Approach: | They propose a model that handles context in many natural language processing areas to solve a problem of extracting opinion references from text. |
| Outcome: | Experiments on tweets that refer to television programs show the proposed model can extract opinion references with more than 90% accuracy. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
| Outcome: | The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech. |
User-Level Race and Ethnicity Predictors from Twitter Text (C18-1)
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| Challenge: | Using social media text to identify user-level race and ethnicity is a useful tool for a range of downstream applications, including passive polling or quantifying demographic bias. |
| Approach: | They propose to collect data from social media users who self-report their race/ethnicity through a survey to develop models which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC. |
| Outcome: | The proposed models accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make available to the research community. |
Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research (2024.emnlp-main)
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| Challenge: | Social media data exhibits distinctive characteristics such as rapid and continual topic evolution. |
| Approach: | They propose new protocols and best practices for improving dataset development from social media data and its usage. |
| Outcome: | The proposed protocols and best practices improve the performance of social media datasets and their usage. |
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
| Outcome: | The proposed model outperforms baseline models which use popularity as informativeness measure. |
Residualized Factor Adaptation for Community Social Media Prediction Tasks (D18-1)
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| Challenge: | Existing approaches to social media language capture only socio-demographic contexts, such as age, education rates, race, and gender. |
| Approach: | They propose a method which integrates community attributes and adapts linguistic features to community attributes. |
| Outcome: | The proposed model integrates community attributes and adapts linguistic features to community attributes. |
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data? (2020.lrec-1)
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| Challenge: | Social media data is a valuable data resource for natural language processing tasks. |
| Approach: | They propose to adapt input text to a more standard form, a task also referred to as normalization. |
| Outcome: | The proposed system scores 94.29 accuracy on the test data compared to 95.22 when trained on human-annotated data. |
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms (2025.acl-long)
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| Challenge: | Social media platforms use machine learning and artificial intelligence to maximize user engagement, but can indirectly cause exposure to harmful content. |
| Approach: | They propose a re-ranking approach using Large Language Models to assess and rerank content sequences using large annotated data sets. |
| Outcome: | The proposed method significantly outperforms existing proprietary moderation methods on three datasets, three models and across three configurations. |
GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)
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| Challenge: | a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text . |
| Approach: | They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap. |
| Outcome: | The proposed model improves on state-of-the-art models on two datasets. |