Much of the research in social computing analyzes data from social media
platforms, which may inherently carry biases. An overlooked source of such bias
is the over-representation of WEIRD (Western, Educated, Industrialized, Rich,
and Democratic) populations, which might not accurately mirror the global
demographic diversity. We evaluated the dependence on WEIRD populations in
research presented at the AAAI ICWSM conference; the only venue whose
proceedings are fully dedicated to social computing research. We did so by
analyzing 494 papers published from 2018 to 2022, which included full research
papers, dataset papers and posters. After filtering out papers that analyze
synthetic datasets or those lacking clear country of origin, we were left with
420 papers from which 188 participants in a crowdsourcing study with full
manual validation extracted data for the WEIRD scores computation. This data
was then used to adapt existing WEIRD metrics to be applicable for social media
data. We found that 37% of these papers focused solely on data from Western
countries. This percentage is significantly less than the percentages observed
in research from CHI (76%) and FAccT (84%) conferences, suggesting a greater
diversity of dataset origins within ICWSM. However, the studies at ICWSM still
predominantly examine populations from countries that are more Educated,
Industrialized, and Rich in comparison to those in FAccT, with a special note
on the 'Democratic' variable reflecting political freedoms and rights. This
points out the utility of social media data in shedding light on findings from
countries with restricted political freedoms. Based on these insights, we
recommend extensions of current "paper checklists" to include considerations
about the WEIRD bias and call for the community to broaden research inclusivity
by encouraging the use of diverse datasets from underrepresented regions.

社交计算、偏见、WEIRD 人口、数据集来源、研究包容性。