ISCoDe: A framework for interest similarity-based community detection in social networks

Jaho, Eva; Karaliopoulos, Merkourios; Stavrakakis, Ioannis

doi:10.1109/infcomw.2011.5928942

Cited by 21 publications

(14 citation statements)

References 19 publications

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“…Similarly, Mitchell et al (2013) investigate a macro-scale dataset of happiness, urbanization and obesity correlates, but do not create a generalizable model for wide-scale usage. Allen et al (2014) and Jaho et al (2011) investigated how content traversed social graphs, and explored opportunistic mechanisms for the dissemination of content via social structures. A focus of their work was mechanisms for community detection, and subsequent analysis of social structures for observing information paths through social networks.…”

Section: Related Workmentioning

confidence: 99%

How do politicians use Facebook? An applied Social Observatory

2015

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In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Googleþ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: ''zooming'' in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.

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Section: Related Workmentioning

confidence: 99%

How do politicians use Facebook? An applied Social Observatory

2015

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“…Thus, different communities have resulted in these clusters. Each node's distance and its centroid in social networks are determined based on the SO in Formula (5). If the SO (similarity) between two nodes increases, the distance between two nodes decreases.…”

Section: Phase 1: Sorting Nodes By Futuristic Greedymentioning

confidence: 99%

“…(e) In several methods, with the growing number of members on social networks, the volume of data associated with social networks increases, and community detection becomes more complex. 5 There are two main challenges in community detection with big data. The first challenge is the precision decreasing of the discovery of independent communities by the person's interests.…”

Section: Introductionmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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“…Aiming at identifying shared opinions and common interests, they use bird-flocking behavior. Another work from Jaho et al [15] proposes a framework to access similarity in OSN using interest similarity. These works explore data mining techniques and build on the assumption that there are enough processing resources.…”

Section: Related Workmentioning

confidence: 99%

Framework for building intelligent mobile social applications

Maia

Filho

et al. 2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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The use of online social networks (OSN) has been a reality for some years now. If OSN users have mobile devices and they are taken into account, every day a vast amount of information is spontaneously generated, creating opportunities for researchers and developers to use, for example, artificial intelligent techniques to explore similarities between friends, to recommend content and to come up with business opportunities. Additionally, OSN and mobile devices can be put together to explore user context to tailor services according to the user environment. However, to apply ubiquitous computing and artificial intelligence to OSN can be cumbersome and resource-consuming. Thus, this paper presents a framework to develop intelligent applications on top of existing OSN, helping developers to explore similarities between the users and recommend content, using user context and simple information present on OSN, without compromising resources present in mobile devices. At the end, this paper shows how to develop new applications using the framework exploring user context and semantic concepts defined by the developer.

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ISCoDe: A framework for interest similarity-based community detection in social networks

Cited by 21 publications

References 19 publications

How do politicians use Facebook? An applied Social Observatory

How do politicians use Facebook? An applied Social Observatory

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Framework for building intelligent mobile social applications

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