Novel and topical business news and their impact on stock market activities

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that stock prices and transaction volumes exhibited a significant response to a news article when it is novel and topical.Comment: 8 pages, 6 figures, 2 table

Temporal Information Models for Real-Time Microblog Search

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step

Using Big Data to measure tourist sustainability: myth or reality?

The concern about the production of international standards to measure the sustainability of tourism is present today, especially the discourse on the introduction of new sources. This article aims to survey and describe the main approaches and methodologies to use big data to measure tourism sustainability. Successful cases are addressed by explaining the main opportunities and challenges for the creation of official tourist statistics. A comprehensive review of publications regarding this field was carried out by applying the systematic literature review technique. This contributes a knowledge base to destination management organisations to encourage the implementation of official tourism statistics systems using big data.This research was funded by the Xunta de Galicia and the European Union (European Social Fund—FSE) through predoctoral stage grants to universties and public research organisations in Galicia and other organisations of the Galician R+D+I System (2017), grant number ED481A-2017/230S

Tracking public opinion on social media

The increasing popularity of social media has changed the web from a static repository of information into a dynamic forum with continuously changing information. Social media platforms has given the capability to people expressing and sharing their thoughts and opinions on the web in a very simple way. The so-called User Generated Content is a good source of users opinion and mining it can be very useful for a wide variety of applications that require understanding the public opinion about a concept. For example, enterprises can capture the negative or positive opinions of customers about their services or products and improve their quality accordingly. The dynamic nature of social media with the constantly changing vocabulary, makes developing tools that can automatically track public opinion a challenge. To help users better understand public opinion towards an entity or a topic, it is important to: a) find the related documents and the sentiment polarity expressed in them; b) identify the important time intervals where there is a change in the opinion; c) identify the causes of the opinion change; d) estimate the number of people that have a certain opinion about the entity; and e) measure the impact of public opinion towards the entity. In this thesis we focus on the problem of tracking public opinion on social media and we propose and develop methods to address the different subproblems. First, we analyse the topical distribution of tweets to determine the number of topics that are discussed in a single tweet. Next, we propose a topic specific stylistic method to retrieve tweets that are relevant to a topic and also express opinion about it. Then, we explore the effectiveness of time series methodologies to track and forecast the evolution of sentiment towards a specific topic over time. In addition, we propose the LDA & KL-divergence approach to extract and rank the likely causes of sentiment spikes. We create a test collection that can be used to evaluate methodologies in ranking the likely reasons of sentiment spikes. To estimate the number of people that have a certain opinion about an entity, we propose an approach that uses pre-publication and post- publication features extracted from news posts and users' comments respectively. Finally, we propose an approach that propagates sentiment signals to measure the impact of public opinion towards the entity's reputation. We evaluate our proposed methods on standard evaluation collections and provide evidence that the proposed methods improve the performance of the state-of-the-art approaches on tracking public opinion on social media

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA