Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Real-time Event Detection Using Self-Evolving Contextual Analysis (SECA) Approach

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Breadth analysis of Online Social Networks

This thesis is mainly motivated by the analysis, understanding, and prediction of human behaviour by means of the study of their digital fingeprints. Unlike a classical PhD thesis, where you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth analysis on the research topic of complex networks, such as those that humans create themselves with their relationships and interactions. These kinds of digital communities where humans interact and create relationships are commonly called Online Social Networks. Then, (i) we have collected their interactions, as text messages they share among each other, in order to analyze the sentiment and topic of such messages. We have basically applied the state-of-the-art techniques for Natural Language Processing, widely developed and tested on English texts, in a collection of Spanish Tweets and we compare the results. Next, (ii) we focused on Topic Detection, creating our own classifier and applying it to the former Tweets dataset. The breakthroughs are two: our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy, outperforming previous results. After that, (iii) we moved to analyze the network structure (or topology) and their data values to detect outliers. We hypothesize that in social networks there is a large mass of users that behaves similarly, while a reduced set of them behave in a different way. However, specially among this last group, we try to separate those with high activity, or low activity, or any other paramater/feature that make them belong to different kind of outliers. We aim to detect influential users in one of these outliers set. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected os of shape, magnitude, amplitude or combination of those. We applied this method to a subset of roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier users. Finally, (iv) we find interesting to address the monitorization of real complex networks. We created a framework to dynamically adapt the temporality of large-scale dynamic networks, reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.PublicadoPrograma de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Rosa María Benito Zafrilla.- Secretario: Ángel Cuevas Rumín.- Vocal: José Ernesto Jiménez Merin

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Purpose: Food companies and consumers are increasingly interested in healthy food and beverages. Coffee is one of the most commonly consumed beverages worldwide. There is increasing consensus that coffee consumption can have beneficial effects on human body. This paper aims at exploring Twitter messages' content and sentiment towards health attributes of coffee. Design/methodology/approach: The research adopted a utilitarian and hedonic consumer behaviour perspective to analyse online community messages. A sample of 13,000 tweets, from around 4,800 users, that mentions keywords coffee and health was collected on a daily basis for a month in mid-2017. The tweets were categorized with a term frequency analysis, keyword-in-context analysis and sentiment analysis. Findings: Results showed that the majority of tweets are neutral or slightly positive towards coffee’s effects on health. Media and consumers are dynamic Twitter users. Findings support that coffee consumption brings favourable emotions, wellness, energy, positive state of mind and an enjoyable and trendy lifestyle. Many tweets have a positive perception of coffee health benefits, especially relating to mental and physical well-being. Research limitations/implications: The high number of users and tweets analysed compensates the limited amount of time of data collection, Twitter messages' restricted number of characters and quantitative software analysis limitations. Practical implications: The research provides valuable suggestions for food and beverage industry managers. Originality/value: This work adds value to the literature by expanding scholars' research on food product attributes perception analysis by using social media as a source of information. Moreover, it provides valuable information on marketable coffee attributes