<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.</p></abstract>

Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.

MapReduce and Semantics Enabled Event Detection using Social Media

3M Corporate Research Laboratory, 3M Corporate Headquarters3M Center, St. Paul, MN 55144

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"MapReduce and Semantics Enabled Event Detection using Social Media"}

Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is...

MapReduce and Semantics Enabled Event Detection using Social Media

Data publikacji: 20 mar 2017

Zakres stron: 201 - 213

Otrzymano: 01 sty 2016

Przyjęty: 04 lip 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0014

Słowa kluczowe
event detection, social media, semantic relatedness, MapReduce

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

MapReduce and Semantics Enabled Event Detection using Social Media

Data publikacji: 20 mar 2017

Zakres stron: 201 - 213

Otrzymano: 01 sty 2016

Przyjęty: 04 lip 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0014

Słowa kluczoweevent detection, social media, semantic relatedness, MapReduce

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
event detection, social media, semantic relatedness, MapReduce