Event detection on large social media using temporal analysis

The problem of event detection based on social media has attracted researchers’ attention recently because of the enormous popularity of social media. Existing approaches focus on features that don’t reflect full characteristics of the social network. For the purpose of this research, we define an event as an occurrence that has enough force and momentum that could create an observable change of the context of a social network. Such a definition provides us with a wider perspective through which we can view the big picture of the social network. In this research, we propose a novel framework for detecting events on social media. We introduce a temporal approach to detect structural change of the social network that reflects an occurrence of an event using machine learning algorithms. In this study, we show that processing temporal social networks captures the complete complexity of the social network, which results in a higher accuracy of event detection

Temporal Data Analysis for Event Detection in Social Media

This research examines the possibility of detecting events using very basic statistical tools. We perform our analysis on a sample dataset collected from Twitter. We show that due to the large amount of data generated by active twitter users, simple statistical tools could be use to detect patterns that represent social events. We have collected posts generated by users over three weeks time period starting from Sep. 17 until Nov. 20. We have filtered these data by collecting only ’NFL’ related posts using twitter API. The amount of tweets was more than 4.4 million tweets. The size of the dataset was more than 17 Gb. We show in our analysis that events could be detected using basic social media network analysis. We’ve been able to successfully detect events by only using volume and frequency analysis

Optimizing Key Distribution in Peer to Peer Network Using B-Trees

Peer to peer network architecture introduces many desired features including self-scalability that led to achieving higher efficiency rate than the traditional server-client architecture. This was contributed to the highly distributed architecture of peer to peer network. Meanwhile, the lack of a centralized control unit in peer to peer network introduces some challenge. One of these challenges is key distribution and management in such an architecture. This research will explore the possibility of developing a novel scheme for distributing and managing keys in peer to peer network architecture efficiently

How to Implement Marketing 2.0 Successfully

The purpose of this research is to develop a model that would close the gap between marketing plans and strategies from one side and the advanced online collaboration applications platforms known as WEB 2.0 in order to implement marketing 2.0 smoothly without disrupting the working environment. We started by examining published articles related to marketing, Web 2.0, Customer Relationship Management Systems, CRM, and social media in a step to conduct an extensive review of the available literature. Then, we presented critique of the articles we have examined. After that, we’ve been able to develop the model we are proposing in this research. As this paper shows, the proposed model will help in transforming marketing plans and strategies from its traditional approach into, what we would like to call, marketing 2.0 approach smoothly. There are some unavoidable limitations due to the given time and scope constrains. The factors included in the proposed model doesn’t cover every related aspect, however, they cover the most important ones