559 research outputs found
Exploratory Analysis of Pairwise Interactions in Online Social Networks
In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4
on pages 422 to 42
State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism
Overview
This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.
The paper is structured as follows:
Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS).
Part 2 provides an introduction to the key approaches of social media intelligence (henceforth âSOCMINTâ) for counter-terrorism.
Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored.
Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work
Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions
Social network usage is growing exponentially in the most up-to-date decade; though social networks are becoming increasingly popular every day, many users are continuously active social network users. Using Twitter, LinkedIn, Facebook, and other social media sites has become the most convenient way for people. There is an enormous quantity of data produced by users of social networks. The most common part of modern research analysis is instrumental for many social network analysis applications. However, people actively utilize social networking sites and diverse uses of these sites. social media sites handle an immense amount of knowledge and answer these three computational problems, noise, dynamism, and scale. Semantic comprehension of the document, image, and video exchanged in a social network was also an essential topic in network analysis. Utilizing data processing provides vast datasets such as averages, laws, and patterns to discover practical knowledge. Using social media, data analysis was primarily used for machine learning, analysis, information extraction, statistical modelling, data preprocessing, and data interpretation processes. This research intentions to deliver an inclusive overview of social network research and application analyze state-of-the-art social media data analysis methods by reviewing basic concepts, social networks and elements social network research is linked to. Semantic ways of manipulating text in social networks are then clarified, and literature discusses studies before on these themes. Next, the evolving methods in research on social network analysis are discussed, especially in analyzing semantic text on social networks. Finally, subjects and opportunities for future research directions are explained
Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization
[EN] The paper aims to identify the individuals who influence the knowledge sharing processes from an internal social network and to forecast the future knowledge flows that may cross it. Exploratory research is employed, and a four-phase methodology is developed which combines a social network analysis with structural modeling. This is applied to the internal enterprise social network used by a British insurance company. The main results emphasize the most influential groups, their relationships, future knowledge flows, and the connection between the network's heterogeneity and structure, and employees' future knowledge sharing intention. These findings have both theoretical and practical implications. The theory is extended by proving that a social network analysis can be used as a tool for evaluating and predicting future knowledge flows. At the same time, a solution is offered to decision-makers so they will be able to: (i) identify the potential knowledge loss; (ii) determine leaders; (iii) establish who is going to act as a knowledge diffuser, by sharing what they know with their coworkers, and who is going to act as a knowledge repository, by focusing on acquiring increasingly more knowledge; (iv) identify the elements that influence employees' future knowledge sharing intention. (C) 2016 Elsevier Inc. All rights reserved."The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; RodrĂguez RodrĂguez, R.; GĂłmez-Gasquet, P.; Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change. 114:103-118. https://doi.org/10.1016/j.techfore.2016.07.032S10311811
When Infodemic Meets Epidemic: a Systematic Literature Review
Epidemics and outbreaks present arduous challenges requiring both individual
and communal efforts. Social media offer significant amounts of data that can
be leveraged for bio-surveillance. They also provide a platform to quickly and
efficiently reach a sizeable percentage of the population, hence their
potential impact on various aspects of epidemic mitigation. The general
objective of this systematic literature review is to provide a methodical
overview of the integration of social media in different epidemic-related
contexts. Three research questions were conceptualized for this review,
resulting in over 10000 publications collected in the first PRISMA stage, 129
of which were selected for inclusion. A thematic method-oriented synthesis was
undertaken and identified 5 main themes related to social media enabled
epidemic surveillance, misinformation management, and mental health. Findings
uncover a need for more robust applications of the lessons learned from
epidemic post-mortem documentation. A vast gap exists between retrospective
analysis of epidemic management and result integration in prospective studies.
Harnessing the full potential of social media in epidemic related tasks
requires streamlining the results of epidemic forecasting, public opinion
understanding and misinformation propagation, all while keeping abreast of
potential mental health implications. Pro-active prevention has thus become
vital for epidemic curtailment and containment
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