Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

A Framework for Twitter Events Detection, Differentiation and its Application for Retail Brands

We propose a framework for Twitter events detection, differentiation and quantification of their significance for predicting spikes in sales. In previous approaches, the differentiation between Twitter events has mainly been done based on spatial, temporal or topic information. We suggest a novel approach that performs clustering of Twitter events based on their shapes (taking into account growth and relaxation signatures). Our study provides empirical evidence that through events differentiation based on their shape one can clearly identify clusters of Twitter events that contain more information about future sales than the non-clustered Twitter signal. We also propose a method for automatic identification of the optimum event window, solving a task of window selection, which is a common problem in the event study field. The framework described in this paper was tested on a large-scale dataset of 150 million Tweets and sales data of 75 brands, and can be applied to the analysis of time series from other domains

In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales

We study the power of Twitter events to predict consumer sales events by analysing sales for 75 companies from the retail sector and over 150 million tweets mentioning those companies along with their sentiment. We suggest an approach for events identification on Twitter extending existing methodologies of event study. We also propose a robust method for clustering Twitter events into different types based on their shape, which captures the varying dynamics of information propagation through the social network. We provide empirical evidence that through events differentiation based on their shape we can clearly identify types of Twitter events that have a more significant power to predict spikes in sales than the aggregated Twitter signal

European Trading Volumes on Cross-market Holidays

There is anecdotal evidence of reduced trading volume in equity markets when other external markets are not trading. This phenomenon can be called the “cross-market holiday effect,” and this study investigates it in detail, providing evidence for the existence of a strong cross-market holiday effect in the pan-European equity markets. The analysis provides an in-depth examination of other aspects like lagged volumes, market capitalization, or multistep ahead modelling. The trading volumes on dates when there is at least one cross-market holiday are on average 8.5% lower than the volumes of the previous period. There are salient effects when the holiday takes place in a dominant market or when most of the European markets are shut. We test whether the lower trading activity on Monday cross-market holidays is a consequence of the weekend effect or whether the Monday bank holidays push down the Monday trading volume. We report a significantly lower volume associated with the Monday bank holidays, and we argue that the weekend effect has an insignificant impact on the Monday volumes where there is at least one regional cross-market holiday

Expiration day effects on European trading volumes

This study investigates the effect of periodic events, such as the stock index futures and options expiration days and the Morgan Stanley Capital International (MSCI) quarterly index reviews, on the trading volume in the pan-European equity markets. The motivation of this study stems from anecdotal evidence of increased trading volume in the equity markets during the run-up to the index options and futures expiration days and MSCI rebalances. This study investigates this phenomenon in more detail and analyses the trading volumes of seven European stock indices and the MSCI International Pan-Euro Price Index. The analysis features a multi-step ahead volume forecast, which is important for practitioners in order to plan multi-day trades while looking to minimise the market impact. The results confirm higher trading activity on the futures and options expiration days, as well as on the MSCI rebalance day. We report a clear futures and options expiration day effect, which accounts for the Friday effect in terms of larger trading volumes. The MSCI rebalance trading volume is significantly different from the volume of the adjacent months with no MSCI reviews, but they cannot explain the end-of-month effect entirely