Polarized User and Topic Tracking

Digital traces of conversations in micro-blogging platforms and in OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to understand and monitor collective behaviors. In this work, we focus on polarization classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarized users are identified, then polarized keywords are discovered by monitoring the activities of previously classified users. This method thus allows to track users and topics over time. The algorithm is written in Python. Data are stored in MongoDB. Requirements: MongoDB and Pymongo.

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Additional Info
Field Value
Accessibility Both
AccessibilityMode Download
Attribution requirements
Basic rights Download
CreationDate 2016-03-14
Creator Cristina Muntean, cristina.muntean@isti.cnr.it
Dependencies on Other SW
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External Identifier
Field/Scope of use Any use
Github https://github.com/hpclab/PTR---Polarized-User-and-Topic-Tracking
Hosting Environment
Item URL http://data.d4science.org/ctlg/ResourceCatalogue/polarized_user_and_topic_tracking
License term
Owner Cristina Muntean, cristina.muntean@isti.cnr.it
RelatedPaper https://doi.org/10.1145/2911451.2914716
Requirement of non-disclosure (confidentiality mark)
Restrictions on use
Semantic Coverage
Sublicense rights No
Territory of use World Wide
ThematicCluster Text and Social Media Mining
UsageMode Download
system:type SoBigData.eu: Method
Management Info
Field Value
Author Muntean Cristina
Maintainer Muntean Cristina
Version 1
Last Updated 21 April 2017, 16:43 (CEST)
Created 12 September 2016, 16:13 (CEST)