Special Issue "Powering the Future Intelligence - Ambient Social Media Analytics"

Submission Deadline: 15 January 2021 (closed)
Guest Editors
Prof. Ansar Yasar, Hasselt University, Belgium.
Dr. Haroon Malik, Marshall University, USA.
Prof. Stephane Galland, UTBM, France.


Information and communication technology has changed rapidly over the past 20 years with a key development being the emergence of social media and the Internet of Things (IoT). The development allows people and object to sense, analyze and send data and disseminate information in large social communities. Across the globe, mobile devices, social networking platforms such as Twitter, YouTube, Facebook, Tumbler, Instagram and hundreds of microblogging sites in conjunctions with 9 billion Internet of Things devices (exist as of today to a projected 200 billion by 2020) are generating truly mind-boggling data every day. There are 2.5 quintillion bytes of data created each day at our current pace, but that pace is only accelerating. Despite social media, fueled by IoT devices, is now a critical part of the information eco-system, extracting information and intelligence from the traces of IoT devices and social data to aid the decision making process is still

There is a pressing need to advance the state-of-the-art of Ambient Social Media Analytics. Ambient Social Data is generated by wearable devices, smartphones, pervasive IoT devices, and shared/posted on social media platforms. Ambient Social Media Analytic is concerned with developing and evaluating informatics tools and frameworks to collect, monitor, analyze, summarize, and visualize the ambient data; an amalgamation of sensory data coupled with social traces. Ambient analytics research serves several purposes:

• Facilitating conversations and interaction between ubiquitous online communities and

• Extracting useful patterns and intelligence to serve entities that include, but are not limited to, active contributors in ongoing dialogues.

To date, ambient social media analytics face several challenges. First, the ambient social media contains a rich set of data not only from regular posts on the social media platforms, indeed the data originates for wearable devices, smartphones, and IoT devices. Such amalgamation of data has yet not been coherently and systemically addressed in the mining and analytics literature. A few examples include establishing tractability links being ambient devices and actors producing social data traces tagging free from the text by taking in contextual and pervasive context. Second, scarcity of the tools, methodology, approaches, and frameworks to can derive the “wisdom of the crowd” in a context-rich application setting. Third, ambient assisted social meida analytics requires concrete performance measures to support decision making for a wide variety and spectrum of application. There do not yet exist large sets of quantative measures to stratify the needs of a plethora of IoT and social media application. Lastly, ambient social media data are crescendos streams of data, with increasing volume, high velocity, veracity, and variety. Thus, pose a significant challenge to computing in general and semantic computing in particular. 

Future Intelligence, Ambient Networks, Pervasive Computing

Published Papers
  • Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker
  • Abstract Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select… More
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  • Detecting Man-in-the-Middle Attack in Fog Computing for Social Media
  • Abstract Fog computing (FC) is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network (close to the Internet of Things (IoT) devices). Fog nodes provide services in lieu of the cloud. Thus, improving the performance of the network and making it attractive to social media-based systems. Security issues are one of the most challenges encountered in FC. In this paper, we propose an anomaly-based Intrusion Detection and Prevention System (IDPS) against Man-in-the-Middle (MITM) attack in the fog layer. The system uses special nodes known as Intrusion Detection System (IDS) nodes to detect… More
  •   Views:1214       Downloads:1186       Cited by:1        Download PDF