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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
TLDR
The results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval), based on a new dataset, contain over 14,000 English tweets, are presented. Expand
SemEval-2013 Task 2: Sentiment Analysis in Twitter
TLDR
Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask. Expand
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TLDR
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams. Expand
Predicting the Type and Target of Offensive Posts in Social Media
TLDR
The Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, is complied and made publicly available. Expand
SemEval-2015 Task 10: Sentiment Analysis in Twitter
TLDR
The 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. Expand
SemEval-2014 Task 9: Sentiment Analysis in Twitter
TLDR
The Sentiment Analysis in Twitter task is described, a continuation of the last year’s task that ran successfully as part of SemEval2013 and introduced three new test sets: regular tweets, sarcastic tweets, and LiveJournal sentences. Expand
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
TLDR
The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. Expand
Evaluation Measures for the SemEval-2016 Task 4 “Sentiment Analysis in Twitter” (Draft: Version 1.13)
This informal document details the evaluation measures that will be used in SemEval2016 Task 4 “Sentiment Analysis in Twitter”, a revamped edition of SemEval-2015 Task 10 (Rosenthal et al., 2015).Expand
Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations
TLDR
It is found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age prediction. Expand
I Couldn’t Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions
TLDR
This paper shows how different aspects of conversational structure can be used to detect agreement and disagreement in discussion forums, and exploits information about meta-thread structure and accommodation between participants to outperform the same system trained on prior existing in-domain smaller annotated datasets. Expand
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