Predicting crime using Twitter and kernel density estimation

By Matthew S. Gerber. 2014

599 citations recorded by the source corpus.

Subfields: Epidemiology, General Social Sciences, Sociology and Political Science

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In Sociology and Political Science

Citations in the corpus

Partially Generative Neural Networks for Gang Crime Classification with Partial Information (2018, methodology): “Similarly, [6] also uses LDA to extract topics from Twitter but then fuses those topics with kernel density estimation estimates of crime density to show that the Twitter features improve predictability of crime.”
Air pollution as a cause of sleeplessness: Social media evidence from a panel of Chinese cities (2019, background): “For three examples among many: Bollen et al. (2011) show that Twitter mood can be used to add explanatory power to stock market forecasts, Gerber (2014) uses Twitter key words to predict crime patterns, and Gayo-Avello et al. (2011) are among several papers using Twitter to predict elections.”
Social big data: Recent achievements and new challenges (2015, background): “Another novel approach using spatio-temporally tagged tweets for crime prediction is presented by Gerber [102].”
Multi-Task Learning for Spatio-Temporal Event Forecasting (2015, unknown): “We detail the three models below.”
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “ and extract the features that can represent from article sources. For the next step, used from achieved features of LDA to classify a new document by collaborative representation classiﬁer (CRC). In [120] used a statistical topic modeling based on LDA to identify discussion topics among a big city ”
Sentiment analysis using deep learning architectures: a review (2019, background): “…et al. (2017), Arun et al. (2017), Roy et al. (2017) and Singh et al. (2018) Crime prediction Wang et al. (2012b), Rosenfeld and Fornango (2008), Gerber (2014) and Azeez and Aravindhar (2015) Disaster assessment, response, management Ragini et al. (2018), Beigi et al. (2016), Radianti et al.…”