Book ChapterDOI
Automatic crime prediction using events extracted from twitter posts
Xiaofeng Wang,Matthew S. Gerber,Donald E. Brown +2 more
- pp 231-238
TLDR
This approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling, and tested on the task of predicting future hit-and-run crimes.Abstract:
Prior work on criminal incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds incidents of interest. This paper presents a preliminary investigation of Twitter-based criminal incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run incidents uniformly across all days.read more
Citations
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Proceedings ArticleDOI
A Survey on Hate Speech Detection using Natural Language Processing
Anna Schmidt,Michael Wiegand +1 more
TL;DR: A survey on hate speech detection describes key areas that have been explored to automatically recognize these types of utterances using natural language processing and discusses limits of those approaches.
Journal ArticleDOI
A Survey of Techniques for Event Detection in Twitter
Farzindar Atefeh,Wael Khreich +1 more
TL;DR: A survey of techniques for event detection from Twitter streams aimed at finding real‐world occurrences that unfold over space and time and highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.
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Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
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Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
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Big Data for Development: A Review of Promises and Challenges
TL;DR: A new kind of digital divide is created in the use of data-based knowledge to inform intelligent decision-making in developing countries by long-standing structural shortages in the areas of infrastructure, economic resources and institutions.
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
Probabilistic topic models
TL;DR: Surveying a suite of algorithms that offer a solution to managing large document archives suggests they are well-suited to handle large amounts of data.
Journal ArticleDOI
Twitter mood predicts the stock market.
TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
Proceedings ArticleDOI
Predicting the Future with Social Media
TL;DR: It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media.