Center for Medical Ethics and Health Policy Staff Publications
Publication Date
1-1-2024
Journal
Frontiers in Genetics
DOI
10.3389/fgene.2024.1482831
PMID
39834549
PMCID
PMC11743634
PubMedCentral® Posted Date
1-6-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
public understanding of science, social media, kinship analysis, DNA testing, forensic DNA, investigative genetic genealogy
Abstract
Social media sites like X (formerly Twitter) increasingly serve as spaces for the public to discuss controversial topics. Social media can spark extreme viewpoints and spread biased or inaccurate information while simultaneously allowing for debate around policy-relevant topics. The arrest of Joseph J. DeAngelo in April 2018 ignited a barrage of social media conversations on how DNA and genetic genealogy led to the suspect. These conversations continued over the following years as policies changed and as the use of the approach expanded. We examined social media coverage of investigative genetic genealogy (IGG) to characterize the volume and temporal patterns in the topics and sentiments of these public conversations. First, using a data analytics tool Brandwatch Consumer Research, we built flexible search strings to collect tweets from the social media platform Twitter/X for IGG-relevant content published from 2018 to 2022, resulting in 24,209 tweets. Second, we applied informatics tools to the dataset to generate topic clusters and analyze trends in cluster volume and distribution over time to define the top 25 peaks in tweet volume, representing the 25 events that generated the highest volume of conversation over the 5-year period. Third, drawing on the contextual framework of key IGG events, we selected three of the top ten events to code for sentiment along with a randomly sampled subset of tweets across the timeframe. Qualitative coding for position on IGG revealed a majority of tweets were supportive of the use of IGG, but key concerns were also voiced about the ethics of IGG. Over a third of conversations on Twitter/X were on either cases solved or suggestions for use of IGG. We archived the social media data for future research. These data highlight key areas of public support and concern within IGG processes and across application contexts.