Social network data analysis for event detection

Cities concentrate enough Social Network (SN) activity to empower rich models. We present an approach to event discovery based on the information provided by three SN, minimizing the data properties used to maximize the total amount of usable data. We build a model of the normal city behavior which we use to detect abnormal situations (events). After collecting half a year of data we show examples of the events detected and introduce some applications.Peer ReviewedPostprint (published version

A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems

In recent years, social media has become one of the most popular platforms for communication. These platforms allow users to report real-world incidents that might swiftly and widely circulate throughout the whole social network. A social event is a real-world incident that is documented on social media. Social gatherings could contain vital documentation of crisis scenarios. Monitoring and analyzing this rich content can produce information that is extraordinarily valuable and help people and organizations learn how to take action. In this paper, a survey on the potential benefits and applications of event detection with social media data analysis will be presented. Moreover, the critical challenges and the fundamental tradeoffs in event detection will be methodically investigated by monitoring social media stream. Then, fundamental open questions and possible research directions will be introduced

Towards Fully Integrated Real-time Detection Framework for Online Contents Analysis - RED-Alert Approach

Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse online contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be ethically tracked. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used social network analysis, speech recognition, face and object detection besides audio event detection to extract information from online sources that are fed in a complex event processor. We have discussed the challenges and prospects of this work especially the need of analysing online contents while respecting European and national data protection laws notably GDPR

Gradual Network Sparsification and Georeferencing for Location-Aware Event Detection in Microblogging Services

Event detection in microblogging services such as Twitter has become a challenging research topic within the fields of social network analysis and natural language processing. Many works focus on the identification of general events with event types ranging from political news and soccer games to entertainment. However, in application contexts like crisis management, traffic planning, or monitoring people’s mobility during pandemic scenarios, there is a high need for detecting localisable physical events. To address this need, this paper introduces an extension of an existing event detection framework by combining machine learning-based geo-localisation of tweets and network analysis to reveal events from Twitter distributed in time and space. Gradual network sparsification is introduced to improve the detection events of different granularity and to derive a hierarchical event structure. Results show that the proposed method is able to detect meaningful events including their geo-locations. This constitutes a step towards using social media data to inform, for example, traffic demand models, inform about infection risks in certain places, or the identification of points of interest

Using social media data to understand mobile customer experience and behavior

Understanding mobile customer experience and behavior is an important task for cellular service providers to improve the satisfaction of their customers. To that end, cellular service providers regularly measure the properties of their mobile network, such as signal strength, dropped calls, call blockage, and radio interface failures (RIFs). In addition to these passive measurements collected within the network, understanding customer sentiment from direct customer feedback is also an important means of evaluating user experience. Customers have varied perceptions of mobile network quality, and also react differently to advertising, news articles, and the introduction of new equipment and services. Traditional methods used to assess customer sentiment include direct surveys and mining the transcripts of calls made to customer care centers. Along with this feedback provided directly to the service providers, the rise in social media potentially presents new opportunities to gain further insight into customers by mining public social media data as well. According to a note from one of the largest online social network (OSN) sites in the US [7], as of September 2010 there are 175 million registered users, and 95 million text messages communicated among users per day. Additionally, many OSNs provide APIs to retrieve publically available message data, which can be used to collect this data for analysis and interpretation. Our plan is to correlate different sources of measurements and user feedback to understand the social media usage patterns from mobile data users in a large nationwide cellular network. In particular, we are interested in quantifying the traffic volume, the growing trend of social media usage and how it interacts with traditional communication channels, such as voice calls, text messaging, etc. In addition, we are interested in detecting interesting network events from users' communication on OSN sites and studying the temporal aspects - how the various types of user feedback behave with respect to timing. We develop a novel approach which combines burst detection and text mining to detect emerging issues from online messages on a large OSN network. Through a case study, our method shows promising results in identifying a burst of activities using the OSN feedback, whereas customer care notes exhibit noticeable delays in detecting such an event which may lead to unnecessary operational expenses. --Mobile customer experience,social media,text data mining,customer feedback

IDENTIFICATION OF PRACTICAL TRAFFIC VIA DIGITAL MEDIA TWITTER STREAM AND SCRUTINY

In the recent times, social networks have been extensively used as a data source for the event detection. Social networks permit people to generate an identity and allow them share it to construct a community. The resultant social network is a basis for managing of social relationships, discovering users with related interests, and locates content and knowledge entered by several users. We provide an actual monitoring scheme for traffic event recognition from the analysis of Twitter stream. The system was designed from ground as event-driven infrastructure, built on service oriented architecture and obtains tweets from Twitter based on various search criteria such as processes tweets, by application of text mining methods; and performs Tweet classification. The objective is to allocate the suitable class label to every tweet, as associated to traffic event or else not. The traffic detection system was in use for monitoring of numerous areas, allowing for recognition of traffic events more or less in real time, often prior to online web sites

What makes the city pulse

The topics of this thesis are event detection and social network analysis in social media. Our work centres on Geo-tagged User Generated Content (UGC) in Twitter, such as Twitter data generated from the metropolitan area of Dublin Ireland over a one month period of time. In this thesis we address the problem of how to detect small scale unexpected events using UGC both in real-time and retrospectively. We proposed a language-text joint modeling algorithm to cope with the large volume and unstructured nature of UGC. We also demonstrate our discovery of interesting correlations between a Twitter user’s social communities and their mobility patterns. Finally a set of features are proposed for carrying out Twitter user’s account type classification, for the purpose of irrelevant contents filtering. This thesis includes several experimental evaluations using real data from users and shows the performance of our algorithms in event detection and provide evidence for our discoveries