Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
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Creation and evaluation of timelines for longitudinal user posts (2023.eacl-main)
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| Challenge: | Existing methods for segmenting user posts into timelines improve quality and cost of manual annotation. |
| Approach: | They propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user’s behaviour based on their online posting activity. |
| Outcome: | The proposed framework is able to evaluate two different social media datasets and compares with existing models. |
Determining Event Outcomes: The Case of #fail (2020.findings-emnlp)
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| Challenge: | Experimental results show that edibility is easier to predict than outcome quality. |
| Approach: | They use tweets containing #cookingFail or #bakingFails to determine event outcomes in social media. |
| Outcome: | The results show that edibility is easier to predict than outcome quality. |
Point-of-Interest Type Inference from Social Media Text (2020.aacl-main)
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| Challenge: | Using a dataset of 200,000 English tweets, we can predict the type of the place from which a tweet was sent from. |
| Approach: | They propose to analyze a dataset of 200,000 tweets from 2,761 points-of-interest in the U.S. and train classifiers to predict the type of the location a tweet was sent from. |
| Outcome: | The proposed method can predict the type of the location a tweet was sent from and reach a macro F1 of 43.67 across eight classes. |
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (N19-1)
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| Challenge: | Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts. |
| Approach: | They analyze reddit.com data to predict the ultimate controversiality of posts . they use textual content and tree structure of early comments to predict content . |
| Outcome: | The proposed model predicts the ultimate controversiality of posts using features drawn from textual content and tree structure of early comments. |
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts (D19-1)
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| Challenge: | Existing tools for hate speech detection and sentiment analysis cannot detect veiled offensiveness of microaggressions . linguistic subtlety of micro-aggressives has made it difficult to analyze their exact nature . |
| Approach: | They propose a typology of microaggressions based on a subset of data . they propose an objective criterion for annotation and an active-learning procedure . |
| Outcome: | The proposed typology of microaggressions is based on a subset of social media data. |
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. |
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
| Approach: | They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change. |
| Outcome: | The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model. |
TSix: A Human-involved-creation Dataset for Tweet Summarization (L18-1)
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| Challenge: | a new dataset for tweet summarization is available for free. |
| Approach: | They propose a dataset for tweet summarization that uses human annotations to evaluate extractive summarizing methods. |
| Outcome: | The proposed dataset includes six events collected from Twitter . human-annotated gold-standard references facilitate evaluation, the study shows . |
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 . |
| Outcome: | The proposed framework generates more cohesive topics than traditional models. |
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |