Determining citizens’ opinions about stories in the news media: analysing Google, Facebook and Twitter

We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories. We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments

Determining citizens’ opinions about stories in the news media

We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories.  We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments

The Influence of Electronic Word of Mouth in an Online Travel Community on Travel Decisions: A Case Study

As a result of embracing the Internet, online travel communities have become an important information source for travelers. The members of these communities communicate through postings called electronic word-of-mouth (eWOM) the act of sharing information on a particular topic. Electronic word-of-mouth (eWOM) is informal communications among consumers regarding the usage or characteristics of goods and services on the Internet (Litvin, Goldsmith, and Pan, 2008). Furthermore, the influence of eWOM has been found to be influential on consumer purchasing behavior (Guernsey, 2000). Thus, an understanding of the potential of eWOM in online travel communities on travel decisions has implications for tourism marketers as well as researchers. The purpose of this research is to examine a single online travel community in order to conduct an in depth analysis of the influence of eWOM on travel decisions. The study uses online travel community postings (eWOM) to explore the types of travel decisions that are discussed, influence of eWOM on these decisions, the types of members and their specific influence on types of travel decisions, the information types provided by the members, the activity level of members and their influence on travel decisions of other members. Thorn Tree Forum, part of Lonely Planet website is the online travel community studied for this research. In an effort to select a sample that would yield maximum variation, treemaps, and purposeful sampling is used to select eight country forums to use as the framework for collecting community member postings. Postings are collected for an eight month period. Data collection and analysis used a multistep process that included thematic networks, coding for influence and details of information shared among members. The results suggest that eWOM in this online travel community influence travel decisions including accommodation choice, food and beverage recommendations, transportation options, safety of the destination, monetary issues, destination information, and itinerary refinements. Residents were influential in accommodations, food and beverages, and destination information, whereas experienced travelers influenced all types of travel decisions except accommodations. Information types identified include warnings, advice/tips, recommendations, and clarifications. Clarifications were the most influential postings, followed by recommendations and advice/tips. The members were categorized into three types low, medium, and high activity level members. Medium activity level members were the most influential members followed by low and high activity level members. The results of this study provide direction for theoretical development of using online travel communities for travel decision making and provide managerial guidance for utilization of online travel communities for enhancing travel products and destination

What Trends in Chinese Social Media

There has been a tremendous rise in the growth of online social networks all over the world in recent times. While some networks like Twitter and Facebook have been well documented, the popular Chinese microblogging social network Sina Weibo has not been studied. In this work, we examine the key topics that trend on Sina Weibo and contrast them with our observations on Twitter. We find that there is a vast difference in the content shared in China, when compared to a global social network such as Twitter. In China, the trends are created almost entirely due to retweets of media content such as jokes, images and videos, whereas on Twitter, the trends tend to have more to do with current global events and news stories

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

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

Identifying Fake News from the Variables that Governs the Spread of Fake News

Several researchers have attempted to investigate the processes that govern and support the spread of fake news. This paper collates and identifies these variables. This paper then categorises these variables based on three key players that are involved in the process: Users, Content, and Social Networks. The authors conducted an extensive review of the literature and a reflection on the key variables that are involved in the process. The paper has identified a total of twenty-seven variables. Then the paper presents a series of tasks to mitigate or eliminate these variables in a holistic process that could be automated to reduce or eliminate fake news propagation. Finally, the paper suggests further research into testing the method in lab conditions