The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games. In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.
The power of social media for the automated analysis of real-world events has been universally recognized @cite_11 . The large volumes of timely user-generated content in response to public events such as election campaigns allow the extraction of event insights not easily obtainable via alternative means @cite_18 @cite_13 . Social media users act as sensors that empower the development of real-time event detection methodologies @cite_19 @cite_16 . We follow the trends of employing social media for our analysis, and take advantage of location-based services data to automate the impact assessment of a major sports event.
@cite_18 @cite_19 @cite_16 @cite_13 @cite_11
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Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow. User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed content for events that are planned and, therefore, known in advance, across different social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and analyze their effectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr. With the rapid development of Internet, new media, such as blogs, wikis, and social media, become a major platform for information dissemination. Numerous studies focus on event dissemination trend analysis for individual media platform, while very few works are conducted to study the dissemination characteristics in a cross-platform manner. In this work, we propose ESAP, a novel cross-platform approach to analyze the event dissemination trend between social network and search engine simultaneously. ESAP includes three models: an event popularity model based on hot word dynamic weight; a trend similarity model to measure the similarity of event popularity across different platforms over time; and an attention degree model to measure event public attention through time. Experimental results based on four real-world event dissemination datasets (two from Baidu and two from Weibo) produce several interesting findings and validate the effectiveness of ESAP in modeling and analyzing event dissemination trend between social network and search engine from different perspectives. Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. News and events are traditionally broadcasted in an After-the-Fact manner, where the masses react to news formulated by a group of professionals. However, the deluge of information and real-time online social media sites have significantly changed this information input-output cycle, allowing the masses to report real-time events around the world. Specifically, the use of Twitter has resulted in the creation of a digital wealth of knowledge that directly associates to such events. Although governments and industries acknowledge the value of extracting events from the TwitterSphere, unfortunately the sheer velocity and volume of tweets poses significant challenges to the desired event analysis. In this paper, we present our Geo and Temporal Association Creator (GTAC) which extracts structured representations of events from the Twitter stream. GTAC further supports event-level investigative analysis of social media data through interactively visualizing the event indicators (who, when, where, and what). Using GTAC, we are trying to create a near real-time analysis environment for analysts to identify event structures, geographical distributions, and key indicators of emerging events.