- Collected 4,644 notes and 12,761 ratings on Birdwatch from January 23 to March 2021
- Used text mining to obtain note contents
- Used Twitter API to get the account name, followers, followees, account age, and verified status for tweets referenced on Birdwatch

University of Michigan - Ann Arbor

- Data collection
- Key variables
- Content characteristics

Community-Based Fact-Checking on Twitter's Birdwatch Platform - Methodology

Pröllochs, N. (2021). Community-Based Fact-Checking on Twitter's Birdwatch Platform. arXiv preprint arXiv:2104.07175. https://arxiv.org/abs/2104.07175 

Community-Based Fact-Checking on Twitter's Birdwatch Platform

Community-Based Fact-Checking on Twitter's Birdwatch Platform - Data Collection

- Misleading – if a tweet was seen as misleading from Birdwatch notes (1 = yes; 0 = otherwise)
- Text explanation – why a tweet is misleading or not (280 characters max)
- Trustworthy sources – “Did you link the sources you believe most people would consider trustworthy” answered by user writing the Birdwatch note (1 = yes; 0 = otherwise)
- HVotes – number of users who find the Birdwatch note helpful
- Votes – number of ratings on a Birdwatch note (could be voted either helpful or unhelpful)

Community-Based Fact-Checking on Twitter's Birdwatch Platform - Key Variables

- Sentiment – used NRC emotion lexicon that categorizes words into positive or negative emotions and the difference between the positive and negative emotion scores is the final sentiment score 
- Text complexity – used Gunning-Fog index that estimates how many years of schooling is required for a user to understand the explanation, with a larger number indicating higher complexity
- Word count – length of Birdwatch note explanations determined by word count 

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