Geotemporal analysis and topic modelling of Twitter data: study in nine big city areas of Indonesia

2021 ◽  
Author(s):  
Totok Wahyu Wibowo ◽  
Sigit Heru Murti Budi Santosa ◽  
Bowo Susilo ◽  
Taufik Hery Purwanto
Keyword(s):  
Topic Modelling ◽  
Twitter Data ◽  
Big City

Crime Prediction Using Twitter Data

2021 ◽  
Vol 17 (3) ◽  
pp. 62-74
Author(s):  
Lydia Jane G. ◽  
Seetha Hari
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Kernel Density ◽  
Topic Modelling ◽  
Prediction Problem ◽  
Crime Prediction ◽  
Twitter Data ◽  
The World ◽  
Ultimate Source ◽  
Social Media Platforms

As social media platforms are being increasingly used across the world, there are many prospects to using the data for prediction and analysis. In the Twitter platform, there are discussions about any events, passions, and many more topics. All these discussions are publicly available. This makes Twitter the ultimate source to use the data as an augmentation for the decision support systems. In this paper, the use of GPS tagged tweets for crime prediction is researched. The Twitter data is collected from Chicago and cleaned, and topic modelling is applied to the resultant set. Before topic modelling, an algorithm has been developed to identify tweets that are relevant to the crime prediction problem. Once the relevant tweets are identified, topic modelling is applied to find out the major crimes in the different beats of Chicago. Kernel density estimation (KDE) is applied to traditional data. The result of this and topic modelling are used to predict the crime count for each beat using logistic regression.

scholarly journals Exploring Spatiotemporal Characteristics of Twitter data Using Topic Modelling Techniques

2019 ◽  
Vol 1 ◽  
pp. 1-3 ◽  
Author(s):  
Young-Hoon Kim ◽  
Hyun-Jee Woo
Keyword(s):  
Spatial Data ◽  
Activity Patterns ◽  
Spatiotemporal Patterns ◽  
Topic Modelling ◽  
Human Beings ◽  
Mobile Environment ◽  
Twitter Data ◽  
Modelling Techniques ◽  
Twitter Users ◽  
The Republic

<p><strong>Abstract.</strong> This research purpose aims to explore the spatiotemporal aspects of social media data with Twitter data by using topic modelling techniques. The spatiotemporal limits are restricted in two areas of the Republic of Korea: Seoul and Jeju Island. This paper searches the semantics and geographical place characteristics of the Twitter data, and the semantics and place characteristics are regarded as topics in the topic modelling. This paper also discusses the temporal intensity over different spatial areas and visualizes the spatiotemporal patterns with GIS techniques.</p><p>As Twitter mobility message contains a user’s interests and behavioural patterns in the geo-tagged data corresponding to its location, it is possible to explore geographical locality and the user’s mobility over space by using textual ontological techniques such as topic modelling. Therefore, this paper attempts keywords searching and textural classification to classify the shared spatial activity patterns of the Twitter users. Consequently, two main analysis themes are explored: the tourist activity patterns attracting the visitors in Jeju over time and temporal periodicity for shopping and meal preference in Seoul, respectively.</p><p>In conclusion, this research represents a potential of the social network data that enables to fill the gap of spatiotemporal patterns of human beings over the online and mobile environment. Furthermore, our study confirms social data analysis techniques as an alternative geographical data source that can complement and replace the roles of spatial data, which could not be analysed in the conventional offline data.</p>

Bird Life of a Big City

1918 ◽  
Vol 86 (2218supp) ◽  
pp. 3-3
Author(s):  
Lee S. Crandall
Keyword(s):  
Big City

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

The Design Medan Concert Hall with Metaphor Architecture Approach

2020 ◽  
Vol 4 (1) ◽  
pp. 54-61
Author(s):  
Vinky Rahman ◽  
Muhammad Khairy Humaizy
Keyword(s):  
Secondary Data ◽  
Building Design ◽  
Traditional Music ◽  
Primary Data ◽  
Support Design ◽  
Physical Conditions ◽  
Sound Control ◽  
Strategic Location ◽  
Big City ◽  
Design Ideas

The theater usually has an attractive form to attract the attention of visitors and also has good sound control in the auditorium so as not to cause sound distortion. Performances in Medan are still inadequate to accommodate international performances. Particularly in Medan, the enthusiasm of the community towards art tends to be high, but the facilities of the place lack to accommodate performances. Data collection methods are carried out by collecting primary data through a process of field comparative study and secondary data through literature studies & comparative studies. The design approach used in design studies are analyzing the physical, conditions around the site, potential, the limits that exist on the site, Site and environmental approaches are analysis of site conditions and the best solutions, the user approach is building analysis to meet the need for facilities and quality in accommodating the show, literature studies related to titles and themes and theories that support design ideas. The Metaphor is chosen as a truss design theme to convey the shape of building design by combining metaphorical forms of buildings and the prominence of the same metaphorical theme in the building to those who visit and see buildings to prevent sound distortions by using porous materials. Medan is a big city in Indonesia as a design area with consideration of a strategic location. It is expected that with the presence of this performance center, domestic and foreign tourists and especially Medan people themselves can enjoy the comfort and get to know traditional music and dance in Indonesia.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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