Event Detection in Twitter by Weighting Tweet's Features

2016

Twitter's increasing popularity as a source of up-to-date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed, data throughput, or memory usage. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this article, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

2016

103 Event detection in Tweets Andrei-Bogdan Baran “Alexandru Ioan Cuza” University, Faculty of Computer Science General Berthelot, No. 16 [email protected] Adrian Iftene “Alexandru Ioan Cuza” University, Faculty of Computer Science General Berthelot, No. 16 [email protected] ABSTRACT Twitter is among the fastest-growing online social networking services, with more than 140 million users producing over 400 million tweets per day. It enables users to post status updates (tweets) about a huge variety of topics to a network of followers using various communication services such as cell phones, e-mails, Web interfaces, or other third-party applications. Monitoring and analyzing this rich and continuous usergenerated content can lead to obtaining valuable information about local and global news and events, because virtually, any person witnessing or involved in any event is nowadays able to disseminate realtime information, which can reach the other side of the world as the ev...

Ingénierie des systèmes d'information, 2016

Social media systems have been proven to be valuable platforms for information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and states of nuclear emergencies in Japan in 2011. The behavior leads to an accumulation of an enormous amount of information. However, finding relevant posts can be a challenging task, since the relevance of a post is dependent both on its content, author and tweet's characteristics. Besides identifying tweets that describe a specific type of event is also challenging due to the high complexity and variety of event descriptions. These challenges present a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large-scale data-analysis applications. Taking to account all the difficulties, this paper proposes a new metric to improve the results of the searches in microblogs. It combines content relevance, tweet relevance and author relevance, and develops a Natural Language Processing method for extracting temporal information of events from posts more specifically tweets. Our approach is based on a methodology of temporal markers classes and on a contextual exploration method. To evaluate our model, we built a knowledge management system. Actually, we used a collection of 10 thousand of tweets talking about the current events in 2014 and 2015.

The Computer Journal, 2016

Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.

Web Engineering, 2012

Various applications are developed today on top of microblogging services like Twitter. In order to engineer Web applications which operate on microblogging data, there is a need for appropriate filtering techniques to identify messages. In this paper, we focus on detecting Twitter messages (tweets) that report on social events. We introduce a filtering pipeline that exploits textual features and n-grams to classify messages into event related and non-event related tweets. We analyze the impact of preprocessing techniques, achieving accuracies higher than 80%. Further, we present a strategy to automate labeling of training data, since our proposed filtering pipeline requires training data. When testing on our dataset, this semi-automated method achieves an accuracy of 79% and results comparable to the manual labeling approach.

Research in event detection from the Twitter streaming data has been gaining momentum in the last couple of years. Although such data is noisy and often contains misleading information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in incremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, ...

2015

Twitter's increasing popularity as a source of up to date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed or data throughput. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this paper, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

ArXiv, 2021

The detection of events from online social networks is a recent, evolving field that attracts researchers from across a spectrum of disciplines and domains. Here we report a time-series analysis for predicting events. In particular, we evaluated the frequency distribution of top n-grams of terms over time, focusing on two indicators: high-frequency n-grams over both short and long periods of time. Both indicators can refer to certain aspects of events as they evolve. To evaluate the model’s accuracy in detecting events, we built and used a Twitter dataset of the mostpopular hashtags that surrounded the well-documented protests that occurred at the University of Missouri (Mizzou) in late 2015.

2015

Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a series of measures that we designed to support the quantitative and qualitative comparison of event detection techniques. In order to demonstrate the effectiveness of these measures, we apply them to state-of-the-art event detection techniques as well as baseline approaches using real-world Twitter streaming data.

2022

People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and ill-I extend my thanks to my guide Dr. Mukesh Kumar,