scholarly journals Topic Modeling Based Image Clustering by Events in Social Media

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Bin Xu ◽  
Guoliang Fan ◽  
Dan Yang
Keyword(s):  
Event Detection ◽  
Topic Modeling ◽  
Joint Distribution ◽  
Topic Model ◽  
Geographic Information ◽  
Image Clustering ◽  
Social Event ◽  
Textual Information ◽  
Photo Collections ◽  
Social Event Detection

Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised multimodal topic model to estimate the joint distribution of time, geographic, content, and attached textual information. Then we annotate the missing geographic photos with a predicted geographic coordinate. Experimental results indicate that the clustering performance improved by annotated geographic information.

Graph-Based Multimodal Clustering for Social Event Detection in Large Collections of Images

2014 ◽  
pp. 146-158 ◽  
Author(s):  
Georgios Petkos ◽  
Symeon Papadopoulos ◽  
Emmanouil Schinas ◽  
Yiannis Kompatsiaris
Keyword(s):  
Event Detection ◽  
Social Event ◽  
Social Event Detection

Dual graph regularized NMF model for social event detection from Flickr data

2016 ◽  
Vol 20 (5) ◽  
pp. 995-1015 ◽  
Author(s):  
Zhenguo Yang ◽  
Qing Li ◽  
Wenyin Liu ◽  
Yun Ma ◽  
Min Cheng
Keyword(s):  
Event Detection ◽  
Dual Graph ◽  
Social Event ◽  
Social Event Detection

Multimedia Social Event Detection in Microblog

2015 ◽  
pp. 269-281 ◽  
Author(s):  
Yue Gao ◽  
Sicheng Zhao ◽  
Yang Yang ◽  
Tat-Seng Chua
Keyword(s):  
Event Detection ◽  
Social Event ◽  
Social Event Detection

scholarly journals Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 376 ◽  
Author(s):  
Cornelia Ferner ◽  
Clemens Havas ◽  
Elisabeth Birnbacher ◽  
Stefan Wegenkittl ◽  
Bernd Resch
Keyword(s):  
Event Detection ◽  
Disaster Response ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Geographic Area ◽  
Relevant Information ◽  
Suggested Approach ◽  
Napa Valley ◽  
Source Of Information

In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available.

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