Effective marketing video application by using geotagged Twitter's status metadata

Recently, social networking sites with the capability of micro-blogging have earned many attentions from mobile audience as a favorite way of real-time blogging. People may update their status or even sharing photographs using their mobile phones. Micro-blogging sites, such as Twitter has the capability to add geographical location information using global positioning system. In this paper, we proposed a more effective way of marketing events or products based on user?s location metadata. With the aid of software technology and information system, we proposed to develop an application that will identify the user?s location and send videos that are related to the user current location. Relevant videos will be sent to the user?s mobile phone so that user is aware if there is any promotion or event around them. The videos will be sent through MMS or microblogging?s site status update from the promoters to the user?s phone. In this paper, we describe of how this proposed work can be applied in the real-world environment and also future improvements of this application

Scaling DBSCAN-like algorithms for event detection systems in Twitter

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.Peer ReviewedPostprint (author's final draft

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Is this Twitter event a disaster?

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Social media services such as Twitter have become an important channel for reporting real-world events. For example, they can describe the current situation during a disaster. The decisions in crises management are based on detailed on-site information such as what is happening, where and when an event is happening, and who is involved. Thus, in real applications, monitoring the events over social media will enable to analyse the current overall situation. In this paper, the authors introduce a prototype for real-time Twitter-based natural disaster detection and monitoring. The detection approach is multilingual and calculates a statistical based probability for a potential disaster event. For an automatic geo-referencing of the disaster, the approach applies spatial gridding. On this basis the grid cells are subject to a spatial-thematic clustering which uses a method similar to region growing. The application’s output is an automatically generated email alert, containing specific information on the disaster

An event detection approach based on Twitter hashtags

Twitter is one of the most popular microblogging services in the world. The great amount of information made Twitter an important information channel for people to know and share news. Hashtag is a popular feature when people use Twitter. It can be taken as human labeled information and is useful for people to identify the topic of a tweet. Many researchers have proposed event-detection approaches that can monitor Twitter data and determine whether special events, such as accidents, extreme weather, earthquakes, or crimes, are happening. Although many approaches considered hashtag as one of their features, few of them explicitly focused on the effectiveness of using hashtag on event detection. In this study, we proposed an event detection approach that utilizes hashtags in tweets. We adopted the feature extraction used in STREAMCUBE (Feng et al., 2015) and applied a clustering K-means approach (Lloyd, 1982) to it. The experiments were conducted on 20,514 tweets with 8,616 hashtags collected between November 13, 2015 and November 17, 2015 with general topic of the Paris Attacks. A randomly sampled subset of 200 tweets was also manually labeled by a human subject to verify the approach. Based on the collected tweets, we demonstrated that the K-means approach could perform better than STREAMCUBE in the clustering results. Also, we discussed how to set the K values for the K-means approach to lead to a better clustering performance