Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling

Classification of Tweets Into Facts and Opinions Using Recurrent Neural Networks

In the last few years, the growth rate of the number of people who are active on Twitter has been consistently spiking. In India, even the government agencies have started using Twitter accounts as they feel that they can get connected to a greater number of people in a short span of time. Apart from the social media platforms, there are an enormous number of blogging applications that have popped up providing another platform for the people to share their views. With all this, the authenticity of the content that is being generated is going for a toss. On that note, the authors have the task in hand of differentiating the genuineness of the content. In this process, they have worked upon various techniques that would maximize the authenticity of the content and propose a long short-term memory (LSTM) model that will make a distinction between the tweets posted on the Twitter platform. The model in combination with the manually engineered features and the bag of words model is able to classify the tweets efficiently.

[Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency]

Objetivos.

Elaborar un esquema operativo integral para detectar la información errónea principal sobre el zika distribuida en Twitter® en el 2016; reconstruir las redes por las que se difunde información mediante retuiteo; contrastar la información verídica frente a la errónea con diversos parámetros; e investigar cómo se difundió en las redes sociales la información errónea sobre el zika durante la epidemia.

Métodos.

Revisamos sistemáticamente los 5 000 tuits más retuiteados con información sobre el zika en inglés, definimos “información errónea” a partir de la evidencia, buscamos tuits que tuvieran información errónea y conformamos un grupo equiparable de tuits con información verídica. Elaboramos un algoritmo para reconstruir las redes de retuiteo de 266 tuits con información errónea y 458 tuits equiparables con información verídica. Calculamos y comparamos nueve parámetros para caracterizar la estructura de las redes a varios niveles, entre los dos grupos.

Resultados.

En los nueve parámetros se aprecian diferencias estadísticamente significativas entre el grupo de información verídica y el de información errónea. La información errónea en general se difunde mediante estructuras más sofisticadas que la información verídica. También hay una considerable variabilidad intragrupal.

Conclusiones.

Las redes de difusión de la información errónea sobre el zika en Twitter fueron sustancialmente diferentes que las de información verídica, lo cual indica que la información errónea se sirve de mecanismos de difusión distintos. Nuestro estudio permitirá formar una comprensión más holística de los desafíos que plantea la información errónea sobre salud en las redes sociales.

Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency

Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic.Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of "misinformation," identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorithm to reconstruct retweeting networks for 266 misinformation and 458 comparable real-information tweets. We computed and compared 9 network metrics characterizing network structure across various levels between the 2 groups.Results. There were statistically significant differences in all 9 network metrics between real and misinformation groups. Misinformation network structures were generally more sophisticated than those in the real-information group. There was substantial within-group variability, too.Conclusions. Dissemination networks of Zika misinformation differed substantially from real information on Twitter, indicating that misinformation utilized distinct dissemination mechanisms from real information. Our study will lead to a more holistic understanding of health misinformation challenges on social media.

Criminal Event Ontology Population and Enrichment using Patterns Recognition from Text

Thousands of criminal events are reported in newspapers and social networks every day. They describe violent acts that include actors, places, times, causes and any information concerning them. Verbal and nominal phrases are used to characterize and expose criminal events, which employ an important variety of natural language structures in the newspapers. In addition, causes, times and spaces of criminal events, use linguistic phrases to represent them in text. All of them need to be extracted as a pattern recognition process in order to extract criminal events from text and the information that concerns them. The extracted events, as a knowledge base, are very useful for information retrieval tasks. Therefore, this paper presents an approach based on pattern recognition in order to extract criminal events from Spanish text, by populating and enriching an ontology model. Ontology population and enrichment involve the instantiation of criminal events and their cause relationships. An evaluation process is carried out with a set of manually tagged newspapers with categories of specific events, and shows promising results.