X: Women of color running for US Congress abused more
A recent study by the Center for Democracy and Technology (CDT) and the University of Pittsburgh, has revealed that women of color campaigning for US Congress in 2024, have been subjected to a disproportionate number of attacks on X. The research aimed to "compare the levels of offensive speech and hate speech that different groups of Congressional candidates are targeted with based on race and gender, with a particular emphasis on women of color."
Study analyzed 800,000 tweets over 3 months
The study took a deep dive into 800,000 tweets posted between May 20 and August 23 this year. These tweets included all posts that mentioned a candidate running for Congress with an account on X. The findings showed that over one-fifth of the posts aimed at Black and Asian women candidates "contained offensive language about the candidate."
Black women candidates face more hate speech
The study also found that Black women faced hate speech way more often than other candidates. "On average, less than 1% of all tweets that mentioned a candidate contained hate speech," the report states. "However, we found that African-American women candidates were more likely than any other candidate to be subject to this type of post (4%)," it added.
Offensive speech could deter women of color from running
The CDT's report also looked into "offensive speech," which they defined as "words or phrases that demean, threaten, insult, or ridicule a candidate." While this kind of speech doesn't necessarily break X's rules, the sheer amount of it could make women of color think twice about running for office. The report recommends that X and similar platforms take "specific measures" to lessen these impacts.
Report calls for clear policies and transparency
The report suggests that platforms like X should have clear rules against attacks based on race or gender, and be more transparent about how they deal with such attacks. It also recommends better reporting tools, accountability measures, regular risk assessments focusing on race and gender, and privacy-preserving methods for independent researchers to study their data.
