Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Islamic view towards Bitcoin

This paper proposes to analyze the agent behavior by means of big data extracted from the search engine « Google trends » and Twitter API to visualize the emotions and the manner of thinking about « Bitcoin » in the Islamic context. Two kinds of sentiment measures are constructed. The first is based on the search query of the word « Bitcoin » with religious connotation all over the world from 14/04/2017 to 14/04/2018 in weekly frequency. The second is built on twitter data from 03/04/2018 to 13/04/2018, by using a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations. In the next step, the Granger causality analysis is used to investigate the hypothesis that this sentiment causes the volatility and the returns of « Bitcoin ». The results show that, at a first-level that twitter users of the word « Islamic Bitcoin » improve positive sentiment. Secondly, the Twitter sentiment measure has a significant effect on lagged Bitcoin returns and volatility. Furthermore, this sentimental variable Granger causes Bitcoin returns and volatility.  This study contributes to the literature by studying the influence of the doctrinal view towards Bitcoin on his prices dynamics. Knowing that Bitcoin is a new financial asset and there is a large debate on his compliance with sharia

Using Twitter trust network for stock market analysis

Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers

Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11

Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed