Automatic Crime Prediction Using Events Extracted from Twitter Posts
By Xiaofeng Wang, Matthew S. Gerber, Donald E. Brown. Social Computing, Behavioral Modeling, and Prediction, 2012
369 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/3904df530049540325753f780c4a5aac8ef1c147
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michael Wiegand
- Hau Chan
- Milind Tambe
- Jieping Ye
- Martin Hilbert
- Sungyong Seo
- Nuria Oliver
- Hamed Jelodar
In Information Systems
- Michael Wiegand
- Jieping Ye
- Martin Hilbert
- Sungyong Seo
- Nuria Oliver
- Hamed Jelodar
- Alex Pentland
- Phebe Vayanos
In Sociology and Political Science
- Hau Chan
- Milind Tambe
- Jieping Ye
- Michael Wiegand
- Martin Hilbert
- Sungyong Seo
- Nuria Oliver
- Hamed Jelodar
Citations in the corpus
- Multi-Task Learning for Spatio-Temporal Event Forecasting (2015, methodology): “Event forecasting: Most research in this area focuses on temporal events and ignores the underlying geographical information, such as the forecasting of elections [21, 29], stock market movements [9], disease outbreaks [2, 23], box office ticket sales [6, 35], and crimes [31].”
- Predicting Social Unrest Events with Hidden Markov Models Using GDELT (2017, methodology): “The LogReg model [32] also treats the event prediction as a classification problem.”
- Sentiment analysis using deep learning architectures: a review (2019, background): “…(2018), Xiong et al. (2018a) and Mataoui et al. (2018) Politics Haselmayer and Jenny (2017), Kušen and Strembeck (2017), Kumari and Babu (2017) and Wang et al. (2012a) Demonetization Singh et al. (2017), Arun et al. (2017), Roy et al. (2017) and Singh et al. (2018) Crime prediction Wang et al.…”
- Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “ns and used a simple Naive Bayes classifier based on features derived from an LDA model [157]. 28 Hamed Jelodar et al. Table 9 Impressive works LDA-based to crime prediction Study Year Purpose Dataset [121] 2012 Automatic semantic analysis on Twitter posts A corpus of tweets from Twitter(manual) [120”
- Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting (2017, background): “Problem (3) is a convex problem and can be solved by the FISTA algorithm [6].”
- Partially Generative Neural Networks for Gang Crime Classification with Partial Information (2018, methodology): “Moving beyond crime data, [27] uses latent Dirichlet allocation (LDA) to extract topics from Twitter posts and then develops a general linear model to predict hit-andrun traffic incidents from the topic models.”
- Big Data for Development: A Review of Promises and Challenges (2016, positive): “The language content of Twitter microblogs has been used to approximate cultural identities, international migration and tourism mobility, including in countries like Malaysia, the Philippines, Venezuela and Indonesia (Mocanu et al. 2013), and it has been shown that the 140 character long micro blog”
- A Survey on Hate Speech Detection using Natural Language Processing (2017, background): “One work concerned with crime prediction is Wang et al. (2012).”
- Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data (2014, background): “In this line, Wang et al. [43] proposed the usage of social media to predict criminal incidents.”