Understanding Effect of Sentiment Content Toward Information Diffusion Pattern in Online Social Networks: A Case Study on TweetScope

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings

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Strategic information diffusion through online social networks

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11 ◽

10.1145/2093698.2093786 ◽

2011 ◽

Author(s):

Jen-Ping Huang ◽

Chih-Yu Wang ◽

Hung-Yu Wei

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Information Diffusion ◽

Strategic Information

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Information Diffusion in Social Networks

Social Networking and Community Behavior Modeling ◽

10.4018/978-1-61350-444-4.ch008 ◽

2011 ◽

pp. 146-163 ◽

Cited By ~ 3

Author(s):

Dmitry Zinoviev

Keyword(s):

Social Networks ◽

Information Flow ◽

Online Social Networks ◽

Information Diffusion ◽

Diffusion Processes ◽

Small World ◽

The Social ◽

Strength Of Weak Ties ◽

Strategic Position

The issue of information diffusion in small-world social networks was first systematically brought to light by Mark Granovetter in his seminal paper “The Strength of Weak Ties” in 1973 and has been an area of active academic studies in the past three decades. This chapter discusses information proliferation mechanisms in massive online social networks (MOSN). In particular, the following aspects of information diffusion processes are addressed: the role and the strategic position of influential spreaders of information; the pathways in the social networks that serve as conduits for communication and information flow; mathematical models describing proliferation processes; short-term and long-term dynamics of information diffusion, and secrecy of information diffusion.

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Emergent Data Mining Tools for Social Network Analysis

Open Source Technology ◽

10.4018/978-1-4666-7230-7.ch078 ◽

2015 ◽

pp. 1539-1556

Author(s):

Dhiraj Murthy ◽

Alexander Gross ◽

Alex Takata

Keyword(s):

Data Mining ◽

Social Networks ◽

Social Network ◽

Network Analysis ◽

Online Social Networks ◽

Data Extraction ◽

Future Directions ◽

Effective Development ◽

Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

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Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7137 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13730-13731

Author(s):

Ece C. Mutlu

Keyword(s):

Social Networks ◽

Social Learning ◽

Complex Systems ◽

Bayesian Networks ◽

Online Social Networks ◽

Information Diffusion ◽

Probabilistic Models ◽

Human Interaction ◽

Multi Agent Systems ◽

Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

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