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MOSS 2020 : The International Workshop on Mining Online Social Streams

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Link: https://easychair.org/cfp/moss-2020
 
When Nov 2, 2020 - Nov 5, 2020
Where Madeira, Portugal
Abstract Registration Due Jun 23, 2020
Submission Deadline Jun 30, 2020
Notification Due Aug 2, 2020
Final Version Due Aug 30, 2020
Categories    machine learning   social network analysis   text mining   spam and bot detection
 

Call For Papers

As a result of technological advancements, various aspects of social phenomena are witnessing transformative process at a faster pace. For instance, communication and interaction of people have witnessed a tremendous transformation, especially with the advent of Online Social Networks (OSNs). The online social media is one of the defining phenomena in this technology-driven era. With an estimated 2.46 billion connected users, the OSNs have been instrumental in globalisation and enable socio-technological research to understand modern society better. The quest to turn every aspect of humans' lives into computerised data for competitive value has been gaining momentum and the OSNs provide useful sources of commoditised data at scale. Because users can share information about virtually all aspects of their social life, the OSNs are ideal for studying various aspects of social events.

The International Workshop on Mining Online Social Streams (MOSS) main goal is to enable a platform that will allow experts and professionals to discuss the sophisticatedly evolving social media ecosystem with an emphasis on the following themes: identifying spurious content, clustering and community detection and computational sociometry. It is against this backdrop we would like to welcome submissions covering, but not limited to, the following key areas:

On spurious content

While the OSNs facilitate access to a large collection of diverse data, a substantial amount of it is contributed by spam or fake users. Without a proper data filtering mechanism, the growing dominance of spurious/irrelevant online content undermines the credibility of research based on analysing such data. This theme of the workshop is motivated by the need to identify and filter out spurious content in online social networks. We would like to welcome submissions in the following areas (but not limited to):

(1) Online fake news and conspiracy theories
(2) Detection of rumour, urban legend and hearsay
(3) Propagation and detection of online smear campaigns
(4) Methods for distinguishing spam vs. non-spam social media posts
(5) Detection of social bots and the use of bots to influence public opinions
(6) Distinguishing between real-world and non-real world events
(7) Automated generation of deceitful content
(8) Misinformation and disinformation on social media

The workshop theme is aimed at identifying useful methods and precautionary measures to avoid compromising research outcomes by irrelevant or unrepresentative data.

On clustering and community detection

A complex network is considered as a composition of many sub-networks and one of the vital tasks is to identify the community structure or relevant clusters embedded in the network. The utility of a community structure in enabling effective analysis of complex networks makes it ideal to explore the network by identifying a set of nodes and corresponding relationships. We welcome submissions (but not limited to) in the following areas:

(1) Methods of analysing complex networks
(2) Detection of clusters in transitory networks
(3) Bimodal approach to community detection in social networks

On computational sociometry

With the current data deluge, it will be worthwhile to analyse various forms of relationships and test relevant theories from a social science perspective. In the same vein, submissions are welcome in the following areas (but not limited to):

(1) Identifying unethical behaviour online
(2) Identifying weak and strong relationships online, e.g. casual acquaintances
(3) Multiplexity of relationships and a structural hole in online social networks
(4) Identifying threatening or abusive contents online

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