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The popularity of social networks has rapidly increased over the past few years. Social networks provide many kinds of services and benefits to their users like helping them to communicate, click, view and share contents that reflect their opinions or interests. Detecting important contents defined as the most visited posts and users whom disseminate them can provide some interesting insights from cyberspace user’s activities. In this paper, a framework for discovering important posts (most popular posts by views count) and influential users is introduced. The proposed framework employed on Telegram instant messaging service in this study but it is also applicable to other social networks such as Instagram and Twitter. This framework continuously works in a real social network analysis system named Zekavat to find daily important posts and influential users. The effectiveness of this framework was shown in experiments. The accuracy achieved in the advertisement detection model is 89%. Text-based clustering part of the framework was tested based on the human factor verification and clustering time is less than linear. Graph creation based on publishing relationships is more effective than mention relationship and in this process influential users can be identified in a precise manner.

Keywords: social networks, clustering, LSH, machine learning, important posts, influential users

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