Characterizing the Demographics Behind the #BlackLivesMatter Movement
December 17, 2015 Β· Declared Dead Β· π AAAI Spring Symposia
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Alexandra Olteanu, Ingmar Weber, Daniel Gatica-Perez
arXiv ID
1512.05671
Category
cs.SI: Social & Info Networks
Citations
55
Venue
AAAI Spring Symposia
Last Checked
5 months ago
Abstract
The debates on minority issues are often dominated by or held among the concerned minority: gender equality debates have often failed to engage men, while those about race fail to effectively engage the dominant group. To test this observation, we study the #BlackLivesMatter}movement and hashtag on Twitter--which has emerged and gained traction after a series of events typically involving the death of African-Americans as a result of police brutality--and aim to quantify the population biases across user types (individuals vs. organizations), and (for individuals) across various demographics factors (race, gender and age). Our results suggest that more African-Americans engage with the hashtag, and that they are also more active than other demographic groups. We also discuss ethical caveats with broader implications for studies on sensitive topics (e.g. discrimination, mental health, or religion) that focus on users.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Social & Info Networks
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
π»
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
π»
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
π»
Ghosted
The COVID-19 Social Media Infodemic
R.I.P.
π»
Ghosted
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted