6533b855fe1ef96bd12affca

RESEARCH PRODUCT

A framework for real-time Twitter data analysis

Marco MoranaSalvatore GaglioGiuseppe Lo Re

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPoint (typography)Social networkComputer Networks and Communicationsbusiness.industryComputer sciencePerspective (graphical)Volume (computing)02 engineering and technologycomputer.software_genreSocial sensingSet (abstract data type)Order (business)020204 information systems0202 electrical engineering electronic engineering information engineeringTwitter analysi020201 artificial intelligence & image processingState (computer science)Data miningTopic detectionbusinesscomputer

description

A framework for real-time Twitter data analysisWe propose improvements to the Soft Frequent Pattern Mining (SFPM) algorithmThe stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and timeThe set of keywords used to query Twitter is progressively refined to highlight the user's point of viewComparisons with two state of the art systems Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. In this work we present a system for real-time Twitter data analysis in order to follow popular events from the user's perspective. The method we propose extends and improves the Soft Frequent Pattern Mining (SFPM) algorithm by overcoming its limitations in dealing with dynamic, real-time, detection scenarios. In particular, in order to obtain timely results, the stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and time. Since we aim to highlight the user's point of view, the set of keywords used to query Twitter is progressively refined to include new relevant terms which reflect the emergence of new subtopics or new trends in the main topic. The real-time detection system has been evaluated during the 2014 FIFA World Cup and experimental results show the effectiveness of our solution.

yearjournalcountryeditionlanguage
2016-01-01Computer Communications
https://doi.org/10.1016/j.comcom.2015.09.021