Development of concept and methodology for multi-level monitoring of the state of interethnic relations with the data from social media
Project Head: Olessia Koltsova
The major result of this project is a conceptual approach to and a methodology of system devised to monitor ethnic relations on the Post-Soviet space. Its main goal is to trace, in a semi-automatic way, distribution of discussions about ethnicity in the Russian-language social media over time and space. The primary task of this tracing is early prevention of emerging inter-ethnic conflicts. Our conceptual approach is based on a large number of experiments resulting in an integral vision of a sequence of steps necessary for accomplishment of all monitoring tasks. Those steps been translated into a system of concrete methods and algorithms, and they in turn have been implemented in a user-friendly software available online. This easy to use system is accompanied by methodological recommendations that contain both the description of the underlying approach and a practical guide for analysts and researchers interested in monitoring ethnic relations.
Annotation of the project...2017
Publications:
1. Bodrunova S., Koltsova O., Koltsov S., Nikolenko S. Who’s Bad? Attitudes Toward Resettlers From the Post-Soviet South Versus Other Nations in the Russian Blogosphere // International Journal of Communication, 2017 г. Volume 11, pp.3242-3264.
2. Koltsova O., Alexeeva S., Nikolenko S., Koltsov M. Measuring Prejudice and Ethnic Tensions in User-Generated Content // Annual Review of CyberTherapy and Telemedicine, 2017 г. Скачать допечатную версию
3. Koltsova O., Koltsov S., Alexeeva S., Nagornyy O., Nikolenko S. Detecting interethnic relations with the data from social media // Digital Transformation and Global Society. DTGS 2017. Communications in Computer and Information Science, 2017 г. Volume 745. Pp.16-30.
Materials:
Methodical recommendations to the user of the monitoring system
List of correction factors for regions
Lists of linguistic markers of all aspects of the relationship
Classifiers trained to identify other aspects not built into the online system
Soft:
User's manual WebTopicMiner, and web version of the user manual
Information system TopicMiner 2017
User's manual TopicMiner2017
2016
Publications:
1. M. Apishev, S. Koltsov, O. Koltsova, S.I. Nikolenko, K. Vorontsov. Mining Ethnic Content Online with Additively Regularized Topic Models //Computacion y Sistemas, vol. 20, no. 3, 2016, pp. 387–403
2. Nikolenko S.I. Topic Quality Metrics Based on Distributed Word Representations //Proc. 39th International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2016), 2016, pp. 1029–1032 ( ACM DL).
3. Apishev M. Parallel Non-blocking Deterministic Algorithm for Online Topic Modeling //Accepted to Proc. 5th International Conference on Analysis of Images, Social Networks, and Texts ( AIST 2016), 2016.
4. Bodrunova S. Who’s bad? Attitudes to re-settlers from post-Soviet South versus other nations in the Russian blogosphere // International Journal of Communication (under review).
Materials:
Methodical note "IQBuzz data quality and recommendations for their use" (PDF, 226 Кб)
Description of work on the improvement of the software package TopicMiner in 2016 (PDF, 235 Кб)
A training collection marked out for ethno-relevance (1000 texts out of 7181 texts) (XLSX, 292 Кб)
/The complete collection will be available after the publication of works on it/
/Full dictionaries will be available after the publication of works on them/
List of ethnonyms by group (TXT, 2 Кб)
Soft:
TopicMiner_LINIS
2015
Publications:
Topic modelling for qualitative studies (companion page for the corresponding paper)
TopicMiner (software adjusted for the purposes of monitoring ethnicity)
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