Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> Traditional movie gross predictions are based on numerical and categorical movie data from The Internet Movie Database (IMDB). In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help people to predict movie grosses. By analyzing two different models (regression and k-nearest neighbor models), we find models using only news data can achieve similar performance to those using IMDB data. Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media. <s> BIB003 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. <s> BIB004 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> Can one use cell phones for earthquake early warning? Detecting rare, disruptive events using community-held sensors is a promising opportunity, but also presents difficult challenges. Rare events are often difficult or impossible to model and characterize a priori, yet we wish to maximize detection performance. Further, heterogeneous, community-operated sensors may differ widely in quality and communication constraints. In this paper, we present a principled approach towards detecting rare events that learns sensor-specific decision thresholds online, in a distributed way. It maximizes anomaly detection performance at a fusion center, under constraints on the false alarm rate and number of messages per sensor. We then present an implementation of our approach in the Community Seismic Network (CSN), a community sensing system with the goal of rapidly detecting earthquakes using cell phone accelerometers, consumer USB devices and cloud-computing based sensor fusion. We experimentally evaluate our approach based on a pilot deployment of the CSN system. Our results, including data from shake table experiments, indicate the effectiveness of our approach in distinguishing seismic motion from accelerations due to normal daily manipulation. They also provide evidence of the feasibility of earthquake early warning using a dense network of cell phones. <s> BIB005 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> Social media comprises interactive applications and platforms for creating, sharing and exchange of user-generated contents. The past ten years have brought huge growth in social media, especially online social networking services, and it is changing our ways to organize and communicate. It aggregates opinions and feelings of diverse groups of people at low cost. Mining the attributes and contents of social media gives us an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and sometimes the ability to predict future human related events. In this paper, we firstly discuss the realms which can be predicted with current social media, then overview available predictors and techniques of prediction, and finally discuss challenges and possible future directions. <s> BIB006 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> Prior work on criminal incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds incidents of interest. This paper presents a preliminary investigation of Twitter-based criminal incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run incidents uniformly across all days. <s> BIB007 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Event Prediction <s> With the increase in GPS-enabled devices, socialmedia sites, such as Twitter, are quickly becoming a primeoutlet for timely geo-spatial data. Such data can be leveragedto aid in emergency response planning and recovery operations. Unfortunately, the information overload poses significantdifficulty to the quick discovery and identification ofemergency situation areas. The system tackles this challengeby providing real-time mapping of influence areas based onautomatic analysis of the flow of discussion using languagedistributions. The workflow is then further enhanced throughthe addition of keyword surprise mapping which projects the general divergence map onto specific task-level keywords for precise and focused response. <s> BIB008
Social media has been used for predicting events that will happen in near future. Forecasting the popularity of products, movie box-office, election results, and trends in stock markets are examples of such predictions BIB006 . Prediction is based on features of social media posts. Increase in the number of posts which are related to a topic BIB003 can be indicator of its future popularity. Changes in the patterns of using specific words in a area shows onset of an event BIB008 . Also, sentiment of posts can show future status of a product BIB001 . Crime prediction is also possible by semantic role labeling which is used for both finding the events and entities involved in them BIB007 . Prediction method based on the extracted features can vary based on the problem. Regression method have been used for prediction popularity of posts BIB004 but do not perform well when sentiment data is being used BIB002 . For predicting election results, Tumasjan et al.  use number of tweets mentioning political parties and their sentiments as indicators of popularity and political views toward them  . There is no prediction method with perfect accuracy. However, early detection of natural disasters reduces hazards in nearby locations. For example, quakes in areas with geographic proximity are used to predict earthquakes seconds before they happen BIB005 . This process has been used to build warning systems which will be explained in Section 4.