5 research outputs found
Multimodal Social Media Analysis for Gang Violence Prevention
Gang violence is a severe issue in major cities across the U.S. and recent
studies [Patton et al. 2017] have found evidence of social media communications
that can be linked to such violence in communities with high rates of exposure
to gang activity. In this paper we partnered computer scientists with social
work researchers, who have domain expertise in gang violence, to analyze how
public tweets with images posted by youth who mention gang associations on
Twitter can be leveraged to automatically detect psychosocial factors and
conditions that could potentially assist social workers and violence outreach
workers in prevention and early intervention programs. To this end, we
developed a rigorous methodology for collecting and annotating tweets. We
gathered 1,851 tweets and accompanying annotations related to visual concepts
and the psychosocial codes: aggression, loss, and substance use. These codes
are relevant to social work interventions, as they represent possible pathways
to violence on social media. We compare various methods for classifying tweets
into these three classes, using only the text of the tweet, only the image of
the tweet, or both modalities as input to the classifier. In particular, we
analyze the usefulness of mid-level visual concepts and the role of different
modalities for this tweet classification task. Our experiments show that
individually, text information dominates classification performance of the loss
class, while image information dominates the aggression and substance use
classes. Our multimodal approach provides a very promising improvement (18%
relative in mean average precision) over the best single modality approach.
Finally, we also illustrate the complexity of understanding social media data
and elaborate on open challenges