| Challenge: | a new classification of trolling allows for comment-based analysis from both the trolls' and the responders' perspectives . a trolled's intentions may cause a negative psychological impact on the participants . |
| Approach: | They propose a trolling categorization that allows comment-based analysis from both trolls' and responders' perspectives . they annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolled users . |
| Outcome: | The proposed model allows comment-based analysis from both the trolls' and the responders' perspectives. |
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| Challenge: | Existing methods to counter trolling in online communities are not yet available to address the diversity of trolling behaviors. |
| Approach: | They propose a method for generating counter-responses to trolls by aligning these strategies with human preferences across different trolled contexts. |
| Outcome: | The proposed approach reduces negative effects of trolling and improves the online community environment. |
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls (2022.lrec-1)
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| Challenge: | a new dataset aims to automate the method to counter trolls . trolleds cause psychological damage to individuals and increase social costs . |
| Approach: | They propose to use a dataset to generate counter responses by varying counter responses according to a given strategy. |
| Outcome: | The proposed method improves strategy-controlled sentence generation. |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
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Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
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| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
| Approach: | This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit . |
| Outcome: | This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection . |
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
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| Challenge: | A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. |
| Approach: | They use a large-scale dataset from Chinese microblog Sina Weibo to examine readers' responses to online discussion topics. |
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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. |
Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |
Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions (2020.emnlp-main)
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| Challenge: | Politeness principles play a central role in shaping human interaction. |
| Approach: | They propose a generalized framework for modeling face acts in persuasion conversations using an annotated corpus and computational models. |
| Outcome: | The proposed framework reveals differences in face act utilization between asymmetric roles in persuasion conversations and predicts key conversational outcome. |
Automatic Identification and Classification of Bragging in Social Media (2022.acl-long)
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| Challenge: | Bragging is a speech act employed to build a favorable self-image through positive statements about oneself. |
| Approach: | They propose to use tweets annotated for bragging to build a model that can predict bragging with macro F1 up to 72.42 and 35.95 for binary and multi-class bragging classification tasks respectively. |
| Outcome: | The proposed models predict bragging with macro F1 up to 72.42 and 35.95 in binary and multi-class classification tasks respectively. |
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
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| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |