Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016 - MISNC, SI, DS 2016

2016 ◽  
Keyword(s):  
Social Networks ◽  
Data Science ◽  
International Social

scholarly journals Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 539
Author(s):  
Robin Cohen ◽  
Karyn Moffatt ◽  
Amira Ghenai ◽  
Andy Yang ◽  
Margaret Corwin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Networking ◽  
Older Adult ◽  
Online Social Networks ◽  
Social Behaviour ◽  
Data Science ◽  
Science Studies ◽  
Trust Modeling ◽  
The Future

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.

scholarly journals Social Network Extraction Unsupervised

2021 ◽  
Vol 12 (3) ◽  
pp. 4443-4449
Author(s):  
Mahyuddin K. M. Nasution Et.al
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Information Technology ◽  
Big Data ◽  
Social Network ◽  
Data Science ◽  
Information Sources ◽  
Scientific Data

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

Introduction: What Does Biosensing Do?

Living Data ◽  
2019 ◽  
pp. 1-32
Author(s):  
Celia Roberts ◽  
Adrian Mackenzie ◽  
Maggie Mort ◽  
Theresa Atkinson ◽  
Mette Kragh-Furbo ◽  
...  
Keyword(s):  
Social Networks ◽  
Life Events ◽  
Data Science ◽  
Online Platforms ◽  
Regulatory Problems

Biosensor devices and biosensing practices emerge where health experience, scientific and medical knowledges and online platforms meet. In a synoptic discussion, we map some of these meetings and introduce the approach to health biosensing followed in this book. Our approach questions pervasive elementary assumptions about bodies, time and measurement. It expands to include a gamut of biosensings by comparing experiences of different life events, ranging from conception to ageing.  We flag some of the significant institutional and regulatory problems in aligning scientific and clinical knowledges around biosensors. And we describe the volatile mixing of devices, data science, marketing and social networks on contemporary health platforms in terms of the cultural logics of biosensing.

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