ORIGINAL ARTICLE Human Rights Event Detection from Heterogeneous Social Media Graphs

Human rights organizations are increasingly monitoring social media for identification, verification, and documen-tation of human rights violations. Since manual extraction of events from the massive amount of online social net-work data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights–related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anom-alousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clus-ters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and provides documentation such as links to videos and news reports. Building on our previous work that demon-strates the utility of NPHGS for civil unrest prediction and rare disease outbreak detection, we present an analysis of human rights events detected by NPHGS using two years of Twitter data fromMexico. NPHGS was able to accurately detect relevant clusters of human rights–related tweets prior to international news sources, and in some cases, prior to local news reports. Analysis of social media using NPHGS could enhance the information-gathering missions of human rights organizations by pinpointing specific abuses, revealing events and details that may be blocked from traditional media sources, and providing evidence of emerging patterns of human rights violations. This could lead to more timely, targeted, and effective advocacy, as well as other potential interventions. Key words: big data analytics; data mining; machine learning; social networking Statement of Significanc