Event detection and popularity prediction in microblogging

Zhang, Xiaoming; Chen, Xiaoming; Yan, Cheng; Wang, Senzhang; Li, Zhoujun; Xia, Jiali

doi:10.1016/j.neucom.2014.08.045

Cited by 77 publications

(47 citation statements)

References 36 publications

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“…Some studies like Zhang et al (2015) and Laylavi et al (2016b) showed more interest in user profiles to better understand the origin of tweets. Zhang et al (2015) detected burst words from micro-blogging text streams using term co-occurrence information and user social relation information.…”

Section: Related Workmentioning

confidence: 99%

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Event classification and location prediction from tweets during disasters

et al. 2017

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Social media is a platform to express one's view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

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Section: Related Workmentioning

confidence: 99%

“…Zhang et al (2015) detected burst words from micro-blogging text streams using term co-occurrence information and user social relation information. They proposed a spread model based on the analysis of both event content and user profiles.…”

Section: Related Workmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

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“…In last decade, quantitative understanding the popularity dynamics of online content has been attracting much attention from academia [1][2][3][4][5]. Popularity dynamics represents many real social phenomena, such as video views on YouTube [6][7][8][9][10][11][12][13], reading volume of tweets and news on social media [14][15][16][17][18][19], and movie views on online system [20][21][22].…”

Section: Modeling and Predicting Popularity Dynamicsmentioning

confidence: 99%

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019

J Big Data

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BackgroundWith the rapid development of mobile Internet technology, human society has entered an era of high interconnection between human and information. Especially the emergence of online social networks, such as Weibo in China, LinkedIn, Twitter, Face-book and so on, because of its high interaction and strong group participation, makes many related information converging and merging, leading to the emergence of hot topics on the Internet. These hot topics are the aggregation of information about current events (e.g., natural disasters, sports news, celebrity news), which have geotags-timestamp information. These metadata (geotags and timestamp) are embedded within the content of the message, so that the analysis of an event can be performed by applying a spacetime classification. In addition, the hot topic is the sprout of the network public opinion. Network public opinion has an important impact on the stability of the country and society. Therefore, the popularity of hot topics is studied from the spatial and temporal dimension can better understand the dissemination of public opinion. This paper uses the data of Sina Weibo, one of the largest micro-blogging system in China (https ://weibo .com/), to study the following two questions: (1) study the classification problem of hot topic on Weibo, cluster the popularity time series by clustering algorithm;(2) study the spatial distribution characteristics of hot topic on Weibo based on location label data. AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MAT-LAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Ozdikis et al [10] discussed an event detection method for various topics in Twitter using semantic similarities between hashtags based on clustering. Zhang et al [11] proposed an event detection from online microblogging stream. It combined the normalized term frequency and user's social relation to weight words.…”

Section: Introductionmentioning

confidence: 99%

Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets

Luo¹,

Alqhtani²,

Li³

2018

Machine Learning - Advanced Techniques and Emerging Applications

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A method for detecting hot events such as wildfires is proposed. It uses visual and textual information to improve detection. Starting with picking up tweets having texts and images, it preprocesses the data to eliminate unwanted data, transforms unstructured data into structured data, then extracts features. Text features include term frequencyinverse document frequency. Image features include histogram of oriented gradients, gray-level co-occurrence matrix, color histogram, and scale-invariant feature transform. Next, it inputs the features to the multiple kernel learning (MKL) for fusion to automatically combine both feature types to achieve the best performance. Finally, it does event detection. The method was tested on Brisbane hailstorm 2014 and California wildfires 2017. It was compared with methods that used text only or images only. With the Brisbane hailstorm data, the proposed method achieved the best performance, with a fusion accuracy of 0.93, comparing to 0.89 with text only, and 0.85 with images only. With the California wildfires data, a similar performance was recorded. It has demonstrated that event detection in Twitter is enhanced and improved by combination of multiple features. It has delivered an accurate and effective event detection method for spreading awareness and organizing responses, leading to better disaster management.

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Event detection and popularity prediction in microblogging

Cited by 77 publications

References 36 publications

Event classification and location prediction from tweets during disasters

Event classification and location prediction from tweets during disasters

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets

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