Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Erika Patricia Garces Fernandez, Matthias Hagen, Benno Stein
| Challenge: | Clickbait is a nuisance on social media. |
| Approach: | a corpus of 38,517 annotated Twitter tweets was constructed to detect clickbait . the corpus was annotating tweets on 4-point scale by five annotators at Amazon's Mechanical Turk . |
| Outcome: | The corpus of 38,517 annotated Twitter tweets was used to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017 . |
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| Challenge: | Existing datasets may have different definitions of event or topic, which leads to inconsistent results. |
| Approach: | They present a corpus annotated for event detection tasks consisting of 38 million tweets in French and 130,000 manually annotating tweets as related or unrelated to a given event. |
| Outcome: | The proposed method performs best on 38 million tweets in French and another publicly available dataset of tweets. |
An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages. |
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Know Better – A Clickbait Resolving Challenge (2022.lrec-1)
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| Challenge: | a clickbait headline or teaser is used to "bait" the reader into clicking a link to an article . clickbaiting is annoying but effective, and can be countered with specialized models . |
| Approach: | They propose to construct approaches that can automatically extract relevant information from clickbait articles . they argue that clickbaiting can probably not be defeated with clickbaitting detection alone . |
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Clickbait Spoiling via Question Answering and Passage Retrieval (2022.acl-long)
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| Challenge: | Clickbait is a term used to describe posts intended to entice readers to visit a web page . clickbait spoiling is generating a short text that satisfies the curiosity induced by a clickbaiting post . |
| Approach: | They propose to use clickbait spoiling to generate a short text that satisfies curiosity . they classify the type of spoiler needed and generate appropriate spoilers . |
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A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)
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| Challenge: | Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions. |
| Approach: | They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language . |
| Outcome: | The proposed corpus of Turkish offensive language is the first of its kind in the world . the results show that 19 % of the tweets contain some type of offensive language . |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
| Approach: | They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk . |
| Outcome: | The proposed model outperforms existing models on Twitter and other social media platforms. |
A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles (2023.findings-emnlp)
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| Challenge: | Clickbait detection is a task that aims to automatically detect misleading news titles . despite the importance of the task, there is no publicly available clickbait corpus for Romanian . |
| Approach: | They propose a Romanian Clickbait Corpus that automatically detects misleading news titles . they propose four machine learning methods to establish competitive baselines . |
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TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
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Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |
An Italian Twitter Corpus of Hate Speech against Immigrants (L18-1)
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| Challenge: | a recent study has annotated 6,000 tweets for hate speech against immigrants . the annotation scheme was designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion . |
| Approach: | They describe a Twitter corpus annotated for hate speech against immigrants . they propose a scheme that includes aggressiveness, offensiveness, irony, stereotype and intensity . |
| Outcome: | The proposed annotation scheme includes aggressiveness, offensiveness, irony, stereotype, intensity and (on an experimental basis) intensity. |
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020.lrec-1)
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| Challenge: | Existing work on document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. |
| Approach: | They assemble and publish a multilingual Twitter corpus for the task of hate speech detection using inferred author demographic factors. |
| Outcome: | The results show that the classifiers learn human biases and can be discriminatory towards certain demographic groups. |