Muhammad Atif Qureshi

Exploiting Wikipedia for Entity Name Disambiguation in Tweets

Social media repositories serve as a significant source of evidence when extracting information related to the reputation of a particular entity (e.g., a particular politician, singer or company). Reputation management experts are in need of automated methods for mining the social media repositories (in particular Twitter) to monitor the reputation of a particular entity. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. To address this issue in this paper we use “context phrases” in a tweet and Wikipedia disambiguated articles for a particular entity in a random forest classifier. Furthermore, we also utilize the concept of “relatedness” between tweet and entity using the Wikipedia category-article structure that captures the amount of discussion present inside a tweet related to an entity. The experimental evaluations show a significant improvement over the baseline and comparable performance with other systems representing strong performance given that we restrict ourselves to features extracted from Wikipedia.

Clustering with Error-Estimation for Monitoring Reputation of Companies on Twitter

The aim of this research is to easily monitor the reputation of a company in the Twittersphere. We propose a strategy that organizes a stream of tweets into different clusters based on the tweets’ topics. Furthermore, the obtained clusters are assigned into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource.

Utilizing Microblog Data in a Topic Modelling Framework for Scientific Articles’ Recommendation

Researchers are actively turning to Twitter in an attempt to network with other researchers, and stay updated with respect to various scientific breakthroughs. Young and novice researchers have also found Twitter as a valuable source of information in terms of staying up-to-date with various developments in their field of research. In this paper, we present an approach to utilize this valuable information source within a topic modeling framework to suggest scientific articles of interest to novice researchers. The approach in addition to producing effective recommendations for scientific articles alleviates the cold-start problem and is a step towards elimination of the gap between Twitter and science.

A perspective-aware approach to search: visualizing perspectives in news search results

The result set from a search engine for any users query may exhibit an inherent perspective due to issues with the search engine or issues with the underlying collection. This demonstration paper presents a system that allows users to specify at query time a perspective together with their query. The system then presents results from well-known search engines with a visualization of the results which allows the users to quickly surmise the presence of the perspective in the returned set.