Opinion on Twitter

At a time when news organizations are struggling to grab the attention of audiences in a media-saturated environment, social networking sites (SNS) have created novel opportunities for journalists to connect with followers online—raising questions about how types of social media use might be associated with forging greater connection with users. Just as satire has proven a potent force for attracting audiences to fake news TV programs, it’s reasonable to consider that humor might be an emerging tool for connection in social media spaces where journalists increasingly conduct their work. Through a content analysis of more than 22,000 tweets (or microblog posts), this study examines the extent to which the 430 most-followed journalists on Twitter are using humor—and how such use is associated with other forms of engagement on Twitter. Findings indicate that a journalist’s use of humor is closely associated with sharing opinion and personal life details, and engaging in interpersonal discussion. Moreover, the use of humor is positively related to a journalist’s level of activity on Twitter, suggesting that journalists who become more accustomed to this social space are more apt to adopt its milieu of informality, conversation, and humor. Finally, journalists from less elite news organizations tend to use humor more frequently. These and other findings are discussed in light of challenges facing the journalism field as it negotiates questions of participation and professionalism in digital media spaces.

In the recent, past accurate forecasting of stock market has become a challenging task. The proposed approach helps to achieve high precision in stock market prediction by combining the sensex points, with Really Simple Syndication (RSS) news feeds and Tweets. The algorithm focuses on the correlation between the stock market values, sentiments of tweets and RSS feeds for a particular period of time. In this algorithm, a trained model is used for stock market prediction rates. Experimental study focuses on the stock market sensex prices, RSS news feeds and tweets which are collected for the company ARBK from Amman Stock Exchange (ASE). This paper follows two types of hypothesises. Null Hypothesis H0: Stock level indicators predict the trend of stock rates at an allowable rate of minimum 80% above. Alternate Hypothesis Ha: Stock level indicators along with the sentiment analysis of RSS news feeds and tweets as stock enhances the accuracy of prediction. Our experimental study has proved the correlation between Stock level indicators and RSS news feeds and tweets and has shown significant improvement of 20% in prediction accuracy. .

Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.