Social online marketing is expanding fast with the evolution and recent development in the information and communication technology. Investigating how companies are exploiting social media for marketing, advertisement, and consumer's engagement is gaining more and more interest. In this paper, brands/companies data on Twitter is collected and analyzed to compute the overall company response on Twitter. Responsiveness of a company is inferred from three features: company popularity, average company replies, and average followers' replies. Twitter network features are used in calculating the posting frequency for companies and their followers. It is shown that the proposed approach can be used in finding the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is extracted and the posting behavior of brands and their followers is determined with the help of Twitter network features, such as retweet count and geolocation. This paper contributes to the literature on how Twitter data and its network structure features can be exploited in finding the responsiveness and posting behavior of companies and their followers. We believe that this approach can be used effectively in developing prediction and information-filtering systems, particularly the personalized-recommendation systems.

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Tuberculosis is a contagious disease, but it’s diagnosis is still a difficult and challenging task as it is considered a big threat everywhere on the planet. Literature shows that underdeveloped countries widely use chest radiographs (X ray images) for the diagnosis of tuberculosis. Low accuracy of results and high cost are the two main reasons due to which most of the available methods are not useful for radiologists. In our research, we proposed a detection technique in which features extraction is performed on the basis of their texture, intensity and shape. For evaluating the performance of our proposed methodology, Montgomery Country (MC) dataset is used. It is a publically available data set which consists of 138 CXRs; among them, 80 CXRs are normal and 58 CXRs are malignant. The results of the proposed technique have outperformed state of the art methodologies on the MC dataset as it has shown 81.16% accuracy.