Analyzing Political Discourse in On-line Social Networks
J.UCS Special Issue
Aitor Almeida
(DeustoTech-Deusto Foundation, University of Deusto
Av. Universidades 24, 48007 Bilbao, Spain
aitor.almeida@deusto.es)
Pablo Orduña
(DeustoTech-Deusto Foundation, University of Deusto
Av. Universidades 24, 48007 Bilbao, Spain
pablo.orduna@deusto.es)
On-line Social Networks (OSN) have become a central point of the
Internet. Hundreds of millions of users interact daily on the OSNs,
producing and consuming large amounts of user generated data. The
importance of the OSNs in the political discourse was promptly
recognized during the 2008 USAs presidential campaign, where the usage
of the digital communication channels by the democrats was one of the
differential characteristics. Since then, OSNs have been recognized as
an important channel to reach the target group and as a source of a
trove of user generated information.
On-line Social networks present specific problems and issues during
the data analysis process. These issues raise both from the
demographic distribution of the users of the OSNs, the difference in
the on-line political discourse and the unique technical
characteristics of each network. Traditional techniques have to be
adapted to tackle these unique issues of the OSNs, like the limited
message length, the existence of malicious users or the demographic
bias of the population; in order to take advantage of the real-time
and large scale data available to be analyzed.
The purpose of this special issue is to collect innovative and
high-quality research contributions regarding both the use of OSNs as
a channel for political discourse and campaigns, and the
usefulness of user generated data to assess and predict the
political context. This special collect five high-quality
contributions of this area.
In the first paper, entitled "Generating Politician Profiles based on
Content Analysis of Social Network Datasets", Klara Grčić, Marina
Bagić Babac, and Vedran Podobnik describe a method to generate
extended individual profiles for leading politicians of the European
Union using their activity on Facebook. The authors use these profiles
to perform a comparative analysis between the European Commissioners
and Croatian ministers, showing certain unexpected differences in
their on-line behaviour. Finally, they propose a model for prediction
of on-line political behaviour.
In the second contribution, entitled "Terrorism in the 2015 Election
Period in Turkey: Content Analysis of Political Leaders' Social Media
Activity", Ahmet Güneyli, Metin Ersoy, and Şevki Kıralp conduct a
case-study using the messages posted in Twitter between July and
November 2015 by six political leaders in Turkey and focused mainly on
those about terrorism. Using both a descriptive and qualitative
approach, and a thematic content analysis the authors analyze the
unique approach to terrorism of each one of the leaders.
In the third paper, entitled "On Predicting Election Results using
Twitter and Linked Open Data: The Case of the UK 2010 Election",
Evangelos Kalampokis, Areti Karamanou, Efthimios Tambouris, and
Konstantinos Tarabanis describe a method that combines Twitter data
with Linked Open Data to predict election results. The authors
evaluate their approach using the data from the UK elections of 2010.
In the fourth contribution, entitled "Electoral Preferences Prediction
of the YouGov Social Network Users Based on Computational Intelligence
Algorithms", Sonia Ortiz-Ángeles, Yenny Villuendas-Rey, Itzamá
López-Yáñez, Oscar Camacho-Nieto, and Cornelio Yáñez-Márquez
study 25 classification algorithms to predict voting intentions on the
United States primary presidential elections for 2016, taking as input
the data sets generated by 1200 users of the YouGov OSN, as well as
the answers they gave to an on-line study run by the American National
Election Studies.
In the fifth paper, entitled "Framework for Affective News Analysis of
Arabic News: 2014 Gaza Attacks Case Study", Mahmoud Al-Ayyoub, Huda
Al-Sarhan, Majd Al-So'ud, Mohammad Al-Smadi and Yaser Jararweh present
two mayor contributions: a benchmark annotated dataset of Arabic news
for affective news analysis along with a baseline methodology for the
evaluation of future research in Arabic aspect based sentiment
analysis, and an algorithm for the sentiment classification of Arabic
news. To validate their approach the authors collected news posts and
their related comments are collected from well-known Arabic news
networks such as Al Jazeera and Al Arabiya during the 2014 Gaza
attacks.
We, the editors, would like to thank Dana Kaiser (Editorial Team Lead)
from Journal of Universal Computer Science for her support during the
elaboration of this special issue. We express also our gratitude to
the reviewers for reviewing the manuscripts and providing suggestions
which have helped to improve the quality of the papers. Finally, we
would like to express our sincere thanks to the authors for
contributing their excellent research.
Acknowledgements
The publication of this special issue has been funded
by the Departamento de Educaciín, Universidades e Investigación of
the Basque Government via the DEUSTEK grant.
Aitor Almeida
Pablo Orduña
(Bilbao, March 1st, 2017)
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