nloui / JHU-CUT

Code, data, and models from "Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest" EMNLP 2020 W-NUT

JHU-CUT

Code, data, and models from "Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest" EMNLP 2020 W-NUT

Dataset

The data is in /data. As per Twitter guidelines it only contains the tweet IDs and not the full tweet content.

• keywords_english.txt: Civil unrest-related keywords
• known_annotations.csv: "Cround truth" annotations by the authors used to evaluate Mechanical Turk worker annotations
• labelled_tweets_is_general_unrest.csv: Labels for tweets (IDs only) and whether they were annotated as "general unrest" and "specific/nonspecific event"
• labelled_tweets_is_protest_event.csv: Labels for tweets (IDs only) and whether they were annotated as "specific/nonspecific event"
• majority_annotation_results.csv: All labels for the tweets (IDs along with year and country)

Civil Unrest Event Prediction Models

We evaluated ngram and embedding-based models on how well they can identify tweets discussing specific/nonspecific protests and riots (/data/labelled_tweets_is_protest_event.csv). See the above paper for details.

The below trained models are in /results.

Ngram Models

The Keyword model and Unigram model had F1 0.782 and 0.775 F1, respectively.

• Code: ngram_model.py
• Run settings: run_ngram_models.sh

Note: these scripts handle both the general ngram and civil unrest-related keyword count models.

BERTweet model

This model was not included in the final paper and is still being improved. Currently achieves an F1 of 0.814.

• Code: bertweet_model.py
• Run settings: run_bertweet_model.sh

Note: Using a GPU for BERTweet is highly recommended

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Please email Alexandra DeLucia if you have any issues or questions ([e-mail]).