scholarly journals Mining Public Opinion on Transportation Systems Based on Social Media Data

2019 ◽  
Vol 11 (15) ◽  
pp. 4016 ◽  
Author(s):  
Dawei Li ◽  
Yujia Zhang ◽  
Cheng Li
Keyword(s):  
Social Media ◽  
Public Participation ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transportation Systems ◽  
Web Crawler ◽  
Allocation Model ◽  
Public Opinions ◽  
The Public ◽  
Probabilistic Topic Modeling

Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data.

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

2020 ◽  
Vol 5 (1) ◽  
pp. 86-99
Author(s):  
Runbin Xie ◽  
Samuel Kai Wah Chu ◽  
Dickson Kak Wah Chiu ◽  
Yangshu Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Disease Outbreaks ◽  
Semantic Networks ◽  
Web Crawler ◽  
Public Response ◽  
The Public

AbstractIt is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.

Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities

2018 ◽  
Vol 2672 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao
Keyword(s):  
Social Media ◽  
Prior Knowledge ◽  
Emergency Response ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Hurricane Sandy ◽  
Support Vector ◽  
Twitter Data ◽  
Emergency Events

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

scholarly journals The public accountability of social platforms: lessons from a study on bots and trolls in the Brexit campaign

2018 ◽  
Vol 376 (2128) ◽  
pp. 20180003 ◽  
Author(s):  
Marco Bastos ◽  
Dan Mercea
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ethical Issues ◽  
Lessons Learned ◽  
Information Warfare ◽  
The Public ◽  
Social Media Platforms ◽  
Application Programming ◽  
Access To Data ◽  
The Uk

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

scholarly journals Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data (Preprint)

2020 ◽  
Author(s):  
Junze Wang ◽  
Ying Zhou ◽  
Wei Zhang ◽  
Richard Evans ◽  
Chengyan Zhu
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Vaccine Development ◽  
User Behavior ◽  
Chinese Government ◽  
Web Crawler ◽  
Health Crisis ◽  
User Behavior Analysis ◽  
Sina Weibo ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS We used a web crawler tool and a set of predefined search terms (<i>New Coronavirus Pneumonia</i>, <i>New Coronavirus</i>, and <i>COVID-19</i>) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

scholarly journals Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7115
Author(s):  
Amin Muhammad Sadiq ◽  
Huynsik Ahn ◽  
Young Bok Choi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Activity Analysis ◽  
Physical Activities ◽  
Future Research ◽  
Crowd Sourcing ◽  
Emergency Relief ◽  
The Public ◽  
Text Streams

A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

scholarly journals Crowd Event Perception Based on Spatiotemporal Weber Field

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhou Su ◽  
Hua Wei ◽  
Sha Wei
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Interaction Force ◽  
Motion Modeling ◽  
Allocation Model ◽  
Motion Patterns ◽  
Crowd Management ◽  
Latent Dirichlet Allocation Model ◽  
Spatiotemporal Signal ◽  
Crowd Motion

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. Among the tasks of automatic video-based crowd management, crowd motion modeling is recognized as one of the most critical components, since it lays a crucial foundation for numerous subsequent analyses. However, it still encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, and so forth. Addressing these issues, we propose a novel spatiotemporal Weber field, which integrates both appearance characteristics and stimulus of crowd motion patterns, to recognize the large-scale crowd event. On the one hand, crowd motion is recognized as variations of spatiotemporal signal, and we then measure the variation based on Weber law. The result is referred to as spatiotemporal Weber variation feature. On the other hand, motivated by the achievements in crowd dynamics that crowd motion has a close relationship with interaction force, we propose a spatiotemporal Weber force feature to exploit the stimulus of crowd behaviors. Finally, we utilize the latent Dirichlet allocation model to establish the relationship between crowd events and crowd motion patterns. Experiments on PETS2009 and UMN databases demonstrate that our proposed method outperforms the previous methods for the large-scale crowd behavior perception.

scholarly journals Inferring Concept Prerequisite Relations from Online Educational Resources

2019 ◽  
Vol 33 ◽  
pp. 9589-9594 ◽  
Author(s):  
Sudeshna Roy ◽  
Meghana Madhyastha ◽  
Sheril Lawrence ◽  
Vaibhav Rajan
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Training Data ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Technology Applications ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

scholarly journals A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Meng Cai ◽  
Han Luo ◽  
Ying Cui
Keyword(s):  
Public Health ◽  
Social Media ◽  
Time Series ◽  
Public Opinion ◽  
Public Health Emergency ◽  
Classification Model ◽  
Temporal Networks ◽  
Public Opinions ◽  
The Public ◽  
And Diffusion

With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.

scholarly journals Effective public participation is fundamental for marine conservation - lessons from a large scale MPA

2017 ◽  
Author(s):  
Jon C. Day
Keyword(s):  
Public Participation ◽  
Community Involvement ◽  
Large Scale ◽  
Good Practice ◽  
Marine Conservation ◽  
Environmental Conservation ◽  
Lessons Learned ◽  
The Public ◽  
Marine Park ◽  
Participation Program

The Representative Areas Program (RAP) was, at the time, the most comprehensive process of community involvement and participatory planning for any environmental issue in Australia. The RAP was a key component of the widely acclaimed rezoning of the Great Barrier Reef Marine Park, and although completed in 2003, many lessons learned are still relevant today. This paper provides an analysis of the comprehensive public participation program that significantly influenced the final planning outcome. It provides insights into a fundamental component of effective marine planning, assessing what worked well and what did not in terms of public engagement. Some aspects of the public participation program were innovative, and some were more effective than others. The outcome was one-third of the Marine Park was declared as highly protected no-take zones in 2004, with the remainder of the park also zoned to provide lower levels of protection. The methods used to engage the public and the 25 lessons discussed in this paper should be of interest for practitioners, policy makers and academics elsewhere aiming for ‘good practice’ approaches to achieve environmental conservation.

scholarly journals Preliminary yield estimation of the 2020 Beirut explosion using video footage from social media

Shock Waves ◽  
2020 ◽  
Vol 30 (6) ◽  
pp. 671-675 ◽  
Author(s):  
S. E. Rigby ◽  
T. J. Lodge ◽  
S. Alotaibi ◽  
A. D. Barr ◽  
S. D. Clarke ◽  
...  
Keyword(s):  
Social Media ◽  
Emergency Response ◽  
Large Scale ◽  
Accurate Information ◽  
Time Of Arrival ◽  
Policy Makers ◽  
The Public ◽  
Video Footage ◽  
The Moment ◽  
Clear Line

Abstract Rapid, accurate assessment of the yield of a large-scale urban explosion will assist in implementing emergency response plans, will facilitate better estimates of areas at risk of high damage and casualties, and will provide policy makers and the public with more accurate information about the event. On 4 August 2020, an explosion occurred in the Port of Beirut, Lebanon. Shortly afterwards, a number of videos were posted to social media showing the moment of detonation and propagation of the resulting blast wave. In this article, we present a method to rapidly calculate explosive yield based on analysis of 16 videos with a clear line-of-sight to the explosion. The time of arrival of the blast is estimated at 38 distinct positions, and the results are correlated with well-known empirical laws in order to estimate explosive yield. The best estimate and reasonable upper limit of the 2020 Beirut explosion determined from this method are 0.50 kt TNT and 1.12 kt TNT, respectively.

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