Words are important: A textual content based identity resolution scheme across multiple online social networks

Artificial and Natural Topic Detection in Online Social Networks

iSys - Brazilian Journal of Information Systems ◽

10.5753/isys.2017.329 ◽

2017 ◽

Vol 10 (1) ◽

pp. 80-98

Author(s):

Sylvio Barbon Jr ◽

Gabriel Marques Tavares ◽

Guilherme Sakaji Kido

Keyword(s):

Social Networks ◽

Social Interactions ◽

Online Social Networks ◽

Topic Detection ◽

Privacy And Security ◽

Security Issues ◽

Textual Content

Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs.

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Topic Modeling based on Louvain method in Online Social Networks

10.5753/sbsi.2016.5982 ◽

2016 ◽

Cited By ~ 2

Author(s):

Guilherme Sakaji Kido ◽

Rodrigo Augusto Igawa ◽

Sylvio Barbon Jr.

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Topic Modeling ◽

Textual Content

Online Social Networks (OSNs) are the most used media nowadays, such as Twitter. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside huge volume of data from several themes, topics and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for Topic Modeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph architecture (density and degree) allows the classification of a topic as natural or artificial, this last created by the spammers on OSNs.

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Filtering unwanted posts from online social networks (OSN)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/161032021 ◽

2021 ◽

Vol 10 (3) ◽

pp. 1599-1602

Keyword(s):

Social Networks ◽

Social Networking ◽

Online Social Networks ◽

Personal Space ◽

Online Social Networking ◽

Important Thing ◽

On Line ◽

Index Terms ◽

Textual Content ◽

Filtering Procedure

Now a days, unexpectedly growing using on-line social networks (OSNs). Through this offerings user’s can speak and switch any data. The important thing downside of those Online Social Networking (OSN) offerings is the dearth of privateness for the user’s personal space. We use sample matching and textual content class set of rules for correct filtering results. We suggest a gadget permitting OSN customers to own a right awaymanages at the messages published on their walls. It might be a bendy region that rule primarily based totally gadget are used to lets in customers to customize the filtering procedure implemented to their user’s profiles. A system gaining knowledge of method robotically labeling messages in help of content-primarily based totally filtering. Index Terms: content-primarily based totally filtering, filtering rule, filtering gadget, system gaining knowledge of, on-line social networks

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