metadata

task_categories:
  - text-classification
language:
  - en
task_ids:
  - sentiment-classification
  - hate-speech-detection
size_categories:
  - 10K<n<100K

Tweet Annotation Sensitivity Experiment 2: Annotations in Five Experimental Conditions

Attention: This repository contains cases that might be offensive or upsetting. We do not support the views expressed in these hateful posts.

Description

The dataset contains tweet data annotations of hate speech (HS) and offensive language (OL) in five experimental conditions. The tweet data was sampled from the corpus created by Davidson et al. (2017). We selected 3,000 Tweets for our annotation. We developed five experimental conditions that varied the annotation task structure, as shown in the following figure. All tweets were annotated in each condition.

Condition A presented the tweet and three options on a single screen: hate speech, offensive language, or neither. Annotators could select one or both of hate speech, offensive language, or indicate that neither applied.
Conditions B and C split the annotation of a single tweet across two screens.
- For Condition B, the first screen prompted the annotator to indicate whether the tweet contained hate speech. On the following screen, they were shown the tweet again and asked whether it contained offensive language.
- Condition C was similar to Condition B, but flipped the order of hate speech and offensive language for each tweet.
In Conditions D and E, the two tasks are treated independently with annotators being asked to first annotate all tweets for one task, followed by annotating all tweets again for the second task.
- Annotators assigned Condition D were first asked to annotate hate speech for all their assigned tweets, and then asked to annotate offensive language for the same set of tweets.
- Condition E worked the same way, but started with the offensive language annotation task followed by the hate speech annotation task.

We recruited US-based annotators from the crowdsourcing platform Prolific during November and December 2022. Each annotator annotated up to 50 tweets. The dataset also contains demographic information about the annotators. Annotators received a fixed hourly wage in excess of the US federal minimum wage after completing the task.

Codebook

Column Name	Description	Type
case_id	case ID	integer
duration_seconds	duration of connection to task in seconds	integer
last_screen	last question answered	factor
device	device type	factor
ethn_hispanic	Hispanic race/ethnicity	binary
ethn_white	White race/ethnicity	binary
ethn_afr_american	African-American race/ethnicity	binary
ethn_asian	Asian race/ethnicity	binary
ethn_sth_else	race/ethnicity something else	binary
ethn_prefer_not	race/ethnicity prefer not to say	binary
age	age	integer
education	education attainment 1: Less than high school 2: High school 3: Some college 4: College graduate 5: Master's degree or professional degree (law, medicine, MPH, etc.) 6: Doctoral degree (PhD, DPH, EdD, etc.)	factor
english_fl	English as first language	binary
twitter_use	Twitter use frequency 1: Most days 2: Most weeks, but not every day 3: A few times a month 4: A few times a year 5: Less often 6: Never	factor
socmedia_use	social media use frequency 1: Most days 2: Most weeks, but not every day 3: A few times a month 4: A few times a year 5: Less often 6: Never	factor
prolific_hours	workload on the platform prolific in hours in the last month	integer
task_fun	task perception: fun	binary
task_interesting	task perception: interesting	binary
task_boring	task perception: boring	binary
task_repetitive	task perception: repetitive	binary
task_important	task perception: important	binary
task_depressing	task perception: depressing	binary
task_offensive	task perception: offensive	binary
repeat_tweet_coding	likelihood for another tweet task 1: Not at all likely 2: Somewhat likely 3: Very likely	factor
repeat_hs_coding	likelihood for another hate speech task 1: Not at all likely 2: Somewhat likely 3: Very likely	factor
target_online_harassment	targeted by hateful online behavior	binary
target_other_harassment	targeted by other hateful behavior	binary
party_affiliation	party identification 1: Republican 2: Democrat 3: Independent	factor
societal_relevance_hs	relevance perception of hate speech 1: Not at all likely 2: Somewhat likely 3: Very likely	factor
annotator_id	annotator ID	integer
condition	experimental conditions (A-E)	factor
tweet_batch	tweet ID in batch	factor
hate_speech	hate speech annotation	logical
offensive_language	offensive language annotation	logical
tweet_id	tweet ID	integer
orig_label_hs	number of persons who annotated the tweet as hate speech in the original dataset from Davidson et al. (2017)	integer
orig_label_ol	number of persons who annotated the tweet as offensive language in the original dataset from Davidson et al. (2017)	integer
orig_label_ne	number of persons who annotated the tweet as neither in the original dataset from Davidson et al. (2017)	integer
tweet_hashed	tweet with usernames hashed	character

Citation

If you find the dataset useful, please cite:

@inproceedings{kern-etal-2023-annotation,
    title = "Annotation Sensitivity: Training Data Collection Methods Affect Model Performance",
    author = "Kern, Christoph  and
              Eckman, Stephanie  and
              Beck, Jacob  and
              Chew, Rob  and
              Ma, Bolei  and
              Kreuter, Frauke",
    editor = "Bouamor, Houda  and
              Pino, Juan  and
              Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.992",
    pages = "14874--14886",
}

@inproceedings{beck-etal-2024-order,
    title = "Order Effects in Annotation Tasks: Further Evidence of Annotation Sensitivity",
    author = "Beck, Jacob  and
              Eckman, Stephanie  and
              Ma, Bolei  and
              Chew, Rob  and
              Kreuter, Frauke",
    editor = {V{\'a}zquez, Ra{\'u}l  and
              Celikkanat, Hande  and
              Ulmer, Dennis  and
              Tiedemann, J{\"o}rg  and
              Swayamdipta, Swabha  and
              Aziz, Wilker  and
              Plank, Barbara  and
              Baan, Joris  and
              de Marneffe, Marie-Catherine},
    booktitle = "Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)",
    month = mar,
    year = "2024",
    address = "St Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.uncertainlp-1.8",
    pages = "81--86",
}