The power of twitter on predicting box office revenues

Over the last few years there has been an extraordinary surge of social networking and microblogging services. Twitter is a social network that focuses on social and news media. The Twitter data stream allows access to tweets, timestamps and locations of users. This enables us to capture the trends and patterns of rapidly evolving worldwide events. We use the Twitter data stream for the prediction of consumer preferences in the movie industry and estimate how successful the movie will be in the first and second weekends since its release date. The study provides evidence to suggest that frequencies of contemporaneous tweets and a consensus measure of public sentiment are useful for predicting box-office revenues, implying that any publicity is good publicity in word-of-mouth (WOM) and online viral marketing. Sentiment analysis based on tweets suggests that more extreme sentiment has more impact, and that the more negative the tweets about a movie are, the higher its revenue will be, in contrast with the classic theory of diffusion in news media

Social Media and Forecasting Stock Price Change

The Stock Market is a big influence on both national and international economies. Stock prices are driven by a number of factors: industry performance, company news and performance, investor confidence, micro and macro economic factors like employment rates, wage rates, etc. Stock pricing trends can be gauged from the factors that drive it as well as from the stock\u27s historical performance. As fluctuations in stock prices become more volatile and unpredictable, forecasting models help reduce some of the randomness involved in investing and financial decision making. Users on social media platforms like twitter, StockTwits, and eToro discuss issues related to the stock market. Can the analysis of posts on StockTwits add value to the existing features of stock price predicting models? An existing model that uses twits as features was extended to include sentiment analysis of the text referenced by the URL in the twits to see if the model accuracy did improve. Initial results indicate that the addition of sentiment analysis of the text referenced by the URL does not improve the performance of the model when all twits for a given day are taken into account since the model only identifies the direction of change and not the degree of change. The stock prediction model achieves 65% accuracy compared to the base case accuracy of 44% and augmenting the model with sentiment analysis did not change the accuracy. The study highlights some interesting observations regarding users on the StockTwits social media platform and proposes the need for a domain specific sentiment analyzer in future work

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

When outcome expectations become habitual: explaining vs. predicting new media technology use from a social cognitive perspective

This study examined the triadic relationship between expected outcomes, habit strength, and media technology use within the model of media attendance (Larose & Eastin, 2004). Mobile phone users (N = 664) were divided into two groups using a stratified random sampling method. Respondents of group one (n = 334) were surveyed on existing mobile phone use, respondents of group two (n = 310) were surveyed on the intention to use mobile video phone. On the basis of structural equation analysis, the results of this study support the assumption that within the model of media attendance existing media use is more likely to be explained by habit strength, and new media use is more likely to be predicted by outcome expectations

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Social Media and Hotel E-Marketing in Iran: The Case of Parsian International Hotels

With a quantitative study, this research has aimed to investigate the role of social media in Iranian hotels’ electronic marketing. A questionnaire technique was used on a sample of 140 marketers who work in the Parsian International Hotels’ marketing department. For data evaluation an SPSS program was used. Kolmogorov-Smirnov, Cochran, Regression, Non-standardized coefficients and Standard coefficient tests were carried out. Based on the findings, we can state that social media are still not an important marketing tool for Iranian hotels. Facebook and YouTube are the media which are most used for marketing purposes as videos and photos can be used on these sites more than others. The results show that the marketing abilities of Parsian Hotels improve with the increasing use of social media, but the hotel marketing sector has failed to fully utilize internet opportunity as a marketing tool

Lowering the pirate flag: a TPB study of the factors influencing the intention to pay for movie streaming services

The launch of several movie streaming services has raised new questions about how online consumers deal with both legal and illegal options to obtain their desired products. This paper investigates the factors influencing consumers’ intentions to subscribe to online movie streaming services. These services have challenged the dramatic growth in their illegal counterpart in recent years. Taking the theory of planned behavior as a starting point, we extended existing models in the literature by incorporating factors that are specific to consumer behavior in this particular field. A quantitative survey was conducted for the Italian market, and structural equation modeling was used for data analysis. Attitudes, involvement with products, moral judgement and frequency of past behavior were found to be the most important factors in explaining the intention to pay for movie streaming services. The paper provides insights for policy makers and industry managers on the marketing communication strategies needed to minimize the risk of digital piracy

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc. In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAdvisor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation). As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analyse