Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

An overview study of twitter in the UK local government

Copyright @ 2012 Brunel UniversityMicroblogging applications are becoming a momentous element of the public sector social media agenda. The potential of Twitter to update the public with frequent, concise and real-time content has motivated many pubic authorities to create their accounts, thus generating an interesting topic for research. This paper seeks to make an empirical and methodological contribution to this new body of knowledge by presenting an overview study of general Twitter accounts maintained by UK local government authorities. Over 296,000 tweets were collected from the 187officially listed local government accounts. The analysis was conducted in two stages: an examination of the Twitter networks developed by the accounts was followed by a structural analysis of the tweets. The combination of online research and social media analytics techniques enabled us to reach important conclusions about the use of Twitter by those authorities. The findings indicate high level of maturity of Twitter in the UK local government and point to several directions for further increasing the impact and visibility of those accounts within a social media strategy

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Data-driven Social Mood Analysis through the Conceptualization of Emotional Fingerprints

Abstract A body of knowledge shows the emerging of evidence according to a better account for the emotional spectrum is achievable by employing a complete selection of emotion keywords. Basic emotions, such as Ekman's ones, cannot be considered universal, but are related to with implicit thematic affairs within the corpus under analysis. The paper tracks some preliminary experiments obtained by employing a data-driven methodology that captures emotions, relying on domain data that you want to model. The experimentation consists of investigating the corresponding conceptual space based on a set of terms (i.e., keywords) that are representative of the domain and the determination. Furthermore, the conceptual space is exploited as a bridge between the textual content and its sub-symbolic mapping as an "emotional fingerprint" into a six-dimensional hyperspace