"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper

Predicting Product Performance with Social Media

Last 20 years brought massive growth in IT&C world. Mobile solutions such as netbooks, laptops, mobile phones, tablets enable the wireless connection to the Internet. Anyone can ac-cess it anytime and anywhere. In this context, a part of the activities from the real world have a correspondence in the online discussions. Social media in general and social networks in particular have turned into marketing tools for organizations and a place where people can express their opinions and attitudes about products.The paper shows how social media can be used for predicting the success of a product or service. To showcase this, two case studies are presented; a test to prove that the conversations that take place in social media are a good indicator of success and the second is an exercise to predict the winner of the Oscar for best picture in 2011.Social Media, Social Networks, Prediction, Movie, Internet

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

#oscarssowhite: Millennials, Generation Z And Oscars Viewership

This study explores the declining viewership of nationally broadcasted awards shows through the Oscars in connection with Millennials and Generation Z’s call for diversity and inclusion. This thesis utilizes relationship management theory and corporate reputation theory to explore the current relationship between the Oscars and the Millennial and Generation Z generations. Through a mixed methods qualitative and quantitative process, this study seeks to determine whether Oscars viewership would benefit from the adoption of diversity and inclusion initiatives

Negative Statements Considered Useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statisticalinference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research

Movie Industry Economics: How Data Analytics Can Help Predict Movies’ Financial Success

Purpose: Data analytics techniques can help to predict movie success, as measured by box office sales or Oscar awards. Revenue prediction of a movie before its theatrical release is also an important indicator for attracting investors. While measures for predicting the success of a movie in box office sales and awards are widely missing, this study uses data analytics techniques to present a new measure for prediction of movies’ financial success.Methodology: Data were collected by web-scraping and text mining. Classification and Regression Tree (CART), Random Forests, Conditional Forests, and Gradient Boosting were used and a model for prediction of movies' financial success proposed. Content strategy and generating high profile reviews with complex themes can add to controversy and increase the chance of nomination for major movie awards, including Oscars.Findings/Contribution: Findings show that data analytics is key to predicting the success of movies. Although predicting sales based on data available before the release remains a difficult endeavor, even with state-of-the-art analytics technologies, it potentially reduces the risk of investors, studios and other stakeholders to select successful film candidates and have them chosen before the production process starts. The contribution of this study is to develop a model for predicting box office sales and the chance of nomination for winning Oscars. Practical Implications: Cinema managers and investors can use the proposed model as a guide for predicting movies’ financial success