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Part of book or chapter of book . 2022 . Peer-reviewed
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Twitter Reflections on Syrian Conflict from Turkey

Authors: Selcen Ozturkcan; İnanç Arın; Nihat Kasap; Yücel Saygın;

Twitter Reflections on Syrian Conflict from Turkey

Abstract

The Syrian conflict is a well-known regional conflict, where Turkey is among the most affected countries in political, social, and economic terms. This study explores the Turkish public reflections on the Syria conflict by analyzing 450,000 Tweets posted in the Turkish language between Feb 1, 2015, and Feb 27, 2016. This chapter contributes to the literature by providing a broader perspective with the main research questions of: (1) What are the widely discussed topics on Twitter about Syria by the Turkish users? (2) Were these topics attracting more users to Twitter or encouraging the further engagement of the already existing users? (3) How can the fading-out characteristics of the most popular topics be described? (4) Why - under which conditions and with which features - Tweets about Syria end up extensively re-Tweeted by Turkish users? The authors report a predictive model of 86.12% accuracy to classify high and low Tweets based on the number of re-Tweets received via seven features. The analysis reveals that armed fighting, religious, and political sensitivities within the Turkish public inflate the volume of posted Tweets.

Keywords

Syrian conflict, Turkey, classification, big data, Twitter usage, clustering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green