Minimize Social Network Rumors Based on Rumor Path Tree
ABSTARCT :
Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem.
In this article, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analyze the hazard level that other nodes are potentially affected by when a node is infected by a rumor.
We use the Rumor Path Tree ( RPT ) to analyze the rumor spreading path. By comparing the rumors and truths propagation to a certain node, the steps taken by the rumor node to propagation are estimated. In order to identify the truth node, we construct a fractional function to calculate the effective influence nodes, and select the node with the highest score from the generated RPT pool. Based on the truth node we effectively block the spread of rumors. Finally, experimental results and comparisons on the real datasets prove that our method is effective and efficient.
EXISTING SYSTEM :
One of the interesting features of Twitter is the existence of Rumors Topics, which is a list of top ten most tweeted topics ranked by Twitter’s proprietary algorithm (‘Tweeting’ is a term for writing Twitter messages).
There exists a (non-exhaustive) listing of categories used by studies on social awareness.
The outcomes of these analyses may be used by several applications, such as event monitoring, and opinion mining about products or brands.
DISADVANTAGE :
The purpose of this research is to analyze the views and sentiments of Twitter users on top Rumors social issues of the world in a specific time frame.
In this work, an attempt is made to the mine tweets, capture the political sentiments from it and model it as a supervised learning problem.
Our findings show that social media like Twitter helps to establish a sound perception of the social, political and cultural issues by analyzing the thoughts and feelings of people concerning their comments.
PROPOSED SYSTEM :
In this paper, we propose model which use machine learning algorithm and classify sentiment of twitter message.
It’s reasonable to assume that during a campaign, parties will use Twitter to advertise their candidates, propose party policies, participate in debates and interviews, and also criticize their opponents.
We proposed three main reasons in the previous section, two of them may be the reasons for trend rising, and one may be the reason for trend falling.
ADVANTAGE :
Sentiment analysis has now become an important source in decision-making. People depend upon it for future predictions and efficient working, yet we cannot say it is more than enough to rely upon its judgments because opinions and perceptions of people vary with the circumstances.
They have used one dataset with three algorithms and performance has been evaluated on the basis three different information retrieval metrics precision, recall, and f-measure.
In the proposed supervised learning techniques to classify twitter Rumors topic for that they use text based and network based classifier and conclude C5.0 gave best performance.
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