metadata

annotations_creators:
  - expert-generated
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - n<50K
source_datasets:
  - extended|other
task_categories:
  - text-classification
  - token-classification
  - question-answering
  - other
task_ids:
  - topic-classification
  - named-entity-recognition
  - abstractive-qa
pretty_name: SuperTweetEval
tags:
  - super_tweet_eval
  - tweet_eval
  - natural language understanding

SuperTweetEval

Dataset Card for "super_tweet_eval"

Dataset Description

Homepage: TBA
Repository: TBA
Paper: TBA
Point of Contact: TBA

Dataset Summary

TBA

Dataset Structure

Data Fields

The data fields are the same among all splits.

tweet_topic

text: a string feature.
gold_label_list: a list of string feature.
date: a string feature.

tweet_ner7

text: a string feature.
text_tokenized: a list of string feature.
gold_label_sequence: a list of string feature.
date: a string feature.

tweet_qa

text: a string feature.
gold_label_str: a string feature.
paragraph: a string feature.
question: a string feature.

tweet_intimacy

text: a string feature.
gold_score: a float feature.

tweet_similarity

text_1: a string feature.
text_2: a string feature.
gold_score: a float feature.

tempo_wic

gold_label_binary: a int feature.
word: a string feature.
text_1: a string feature.
text_tokenized_1: a list of string feature.
token_idx_1: a int feature.
date_1: a string feature.
text_2: a string feature.
text_tokenized_2: a list of string feature.
token_idx_2: a int feature.
date_2: a string feature.

tweet_hate

gold_label: a int feature.
text: a string feature.

tweet_emoji

gold_label: a int feature.
text: a string feature.

tweet_sentiment

gold_label: a int feature.
text: a string feature.
target: a string feature.

tweet_nerd

gold_label_binary: a int feature.
target: a string feature.
context: a string feature.
definition: a string feature.
text_start: a int feature.
text_end: a int feature.

Data Splits

task	description	number of instances
tweet_topic	multi-label classification	4585 / 573 / 1679
tweet_ner7	sequence labeling	4616 / 576 / 2807
tweet_qa	generation	9489 / 1086 / 1203
tweet_intimacy	regression on a single text	1191 / 396 / 396
tweet_similarity	regression on two texts	450 / 100 / 450
tempo_wic	binary classification on two texts	1427 / 395 / 1472
tweet_hate	multi-class classification	5019 / 716 / 1433
tweet_emoji	multi-class classification	50,000 / 5,000 / 50,000
tweet_sentiment	ABSA on a five-pointscale	26632 / 4000 / 12379
tweet_nerd	binary classification	* / 407 / *

Citation Information

TweetTopic

@inproceedings{antypas-etal-2022-twitter,
    title = "{T}witter Topic Classification",
    author = "Antypas, Dimosthenis  and
      Ushio, Asahi  and
      Camacho-Collados, Jose  and
      Silva, Vitor  and
      Neves, Leonardo  and
      Barbieri, Francesco",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.299",
    pages = "3386--3400",
    abstract = "Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.",
}

TweetNER7

@inproceedings{ushio-etal-2022-named,
    title = "Named Entity Recognition in {T}witter: A Dataset and Analysis on Short-Term Temporal Shifts",
    author = "Ushio, Asahi  and
      Barbieri, Francesco  and
      Sousa, Vitor  and
      Neves, Leonardo  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.aacl-main.25",
    pages = "309--319",
    abstract = "Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (https://huggingface.co/datasets/tner/tweetner7) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found at https://github.com/asahi417/tner/tree/master/examples/tweetner7{\_}paper).",
}

TweetQA

@inproceedings{xiong2019tweetqa,
  title={TweetQA: A Social Media Focused Question Answering Dataset},
  author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year={2019}
}

TweetIntimacy

@misc{pei2023semeval,
      title={SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis}, 
      author={Jiaxin Pei and Vítor Silva and Maarten Bos and Yozon Liu and Leonardo Neves and David Jurgens and Francesco Barbieri},
      year={2023},
      eprint={2210.01108},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Tweet Similarity

TBA

TempoWiC

@inproceedings{loureiro-etal-2022-tempowic,
    title = "{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media",
    author = "Loureiro, Daniel  and
      D{'}Souza, Aminette  and
      Muhajab, Areej Nasser  and
      White, Isabella A.  and
      Wong, Gabriel  and
      Espinosa-Anke, Luis  and
      Neves, Leonardo  and
      Barbieri, Francesco  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.296",
    pages = "3353--3359",
    abstract = "Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.",
}

TweetHate

@inproceedings{sachdeva-etal-2022-measuring,
    title = "The Measuring Hate Speech Corpus: Leveraging Rasch Measurement Theory for Data Perspectivism",
    author = "Sachdeva, Pratik  and
      Barreto, Renata  and
      Bacon, Geoff  and
      Sahn, Alexander  and
      von Vacano, Claudia  and
      Kennedy, Chris",
    booktitle = "Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.nlperspectives-1.11",
    pages = "83--94",
    abstract = "We introduce the Measuring Hate Speech corpus, a dataset created to measure hate speech while adjusting for annotators{'} perspectives. It consists of 50,070 social media comments spanning YouTube, Reddit, and Twitter, labeled by 11,143 annotators recruited from Amazon Mechanical Turk. Each observation includes 10 ordinal labels: sentiment, disrespect, insult, attacking/defending, humiliation, inferior/superior status, dehumanization, violence, genocide, and a 3-valued hate speech benchmark label. The labels are aggregated using faceted Rasch measurement theory (RMT) into a continuous score that measures each comment{'}s location on a hate speech spectrum. The annotation experimental design assigned comments to multiple annotators in order to yield a linked network, allowing annotator disagreement (perspective) to be statistically summarized. Annotators{'} labeling strictness was estimated during the RMT scaling, projecting their perspective onto a linear measure that was adjusted for the hate speech score. Models that incorporate this annotator perspective parameter as an auxiliary input can generate label- and score-level predictions conditional on annotator perspective. The corpus includes the identity group targets of each comment (8 groups, 42 subgroups) and annotator demographics (6 groups, 40 subgroups), facilitating analyses of interactions between annotator- and comment-level identities, i.e. identity-related annotator perspective.",
}

TweetEmoji

```TBA``

TweetSentiment

@inproceedings{rosenthal-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter",
    author = "Rosenthal, Sara  and
      Farra, Noura  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S17-2088",
    doi = "10.18653/v1/S17-2088",
    pages = "502--518",
    abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.",
}

TweetNERD

@article{mishra2022tweetnerd,
  title={TweetNERD--End to End Entity Linking Benchmark for Tweets},
  author={Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali},
  journal={arXiv preprint arXiv:2210.08129},
  year={2022}
}