Exploring public mood toward commodity markets: a comparative study of user behavior on Sina Weibo and Twitter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenhao Chen ◽  
Kin Keung Lai ◽  
Yi Cai
Keyword(s):  
Crude Oil ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Short Term Memory ◽  
User Behavior ◽  
Commodity Markets ◽  
Allocation Model ◽  
Sina Weibo ◽  
Content Type ◽  
The Public

PurposeSina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices.Design/methodology/approachAn enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts.FindingsBy comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively.Originality/valueTwo sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks.

Entrepreneurship in the digital press: a semantic analysis

2019 ◽  
Vol 26 (3) ◽  
pp. 416-431
Author(s):  
Myriam Martí-Sánchez ◽  
Desamparados Cervantes-Zacarés ◽  
Arturo Ortigosa-Blanch
Keyword(s):  
Empirical Evidence ◽  
Thematic Analysis ◽  
Design Methodology ◽  
Semantic Analysis ◽  
Word Association ◽  
Innovation Ecosystem ◽  
Content Type ◽  
The Public ◽  
The Media ◽  
The Way

Purpose The purpose of this paper is to analyse how the media addresses entrepreneurship and to identify the attributes linked to this phenomenon. Design/methodology/approach The sample is defined in terms of a linguistic corpus comprised of content related to entrepreneurship drawn from the digital editions of the three most important Spanish economic newspapers for the period 2010–2017. Word association and co-occurrence analyses were carried out. Further, a non-supervised clustering process was used as the basis for a thematic analysis. Findings Correspondence between social and media patterns related to the entrepreneurship phenomenon is revealed by the results. It is shown how attributes such as “success”, “innovation”, “ecosystem” and “woman” appear as very relevant and are linked to different co-occurrence scenarios. Relevant thematic groups are also identified related to lexical associations such as innovation, digital economy and public policies linked to entrepreneurship. Research limitations/implications It is important to emphasise that this study has identified and explored relationships between words, but not their evolution. Furthermore, conclusions cannot be drawn concerning whether there are differences in how each newspaper has dealt with entrepreneurship because of the way the corpus was constructed. Originality/value The study provides empirical evidence that helps to identify the way media approaches entrepreneurship. The authors carried out the analysis on the media contents and not on the perception of the public on the phenomenon.

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.

The surveillance of a supreme audit institution on related party transactions

2020 ◽  
Vol 32 (4) ◽  
pp. 577-603
Author(s):  
Gustavo Cesário ◽  
Ricardo Lopes Cardoso ◽  
Renato Santos Aranha
Keyword(s):  
Public Sector ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Conflicts Of Interest ◽  
International Standards ◽  
Content Type ◽  
Related Party Transactions ◽  
The Public ◽  
Audit Reports ◽  
Dirichlet Allocation

PurposeThis paper aims to analyse how the supreme audit institution (SAI) monitors related party transactions (RPTs) in the Brazilian public sector. It considers definitions and disclosure policies of RPTs by international accounting and auditing standards and their evolution since 1980.Design/methodology/approachBased on archival research on international standards and using an interpretive approach, the authors investigated definitions and disclosure policies. Using a topic model based on latent Dirichlet allocation, the authors performed a content analysis on over 59,000 SAI decisions to assess how the SAI monitors RPTs.FindingsThe SAI investigates nepotism (a kind of RPT) and conflicts of interest up to eight times more frequently than related parties. Brazilian laws prevent nepotism and conflicts of interest, but not RPTs in general. Indeed, Brazilian public-sector accounting standards have not converged towards IPSAS 20, and ISSAI 1550 does not adjust auditing procedures to suit the public sector.Research limitations/implicationsThe SAI follows a legalistic auditing approach, indicating a need for regulation of related public-sector parties to improve surveillance. In addition to Brazil, other code law countries might face similar circumstances.Originality/valuePublic-sector RPTs are an under-investigated field, calling for attention by academics and standard-setters. Text mining and latent Dirichlet allocation, while mature techniques, are underexplored in accounting and auditing studies. Additionally, the Python script created to analyse the audit reports is available at Mendeley Data and may be used to perform similar analyses with minor adaptations.

Incorporating LDA with LSTM for followee recommendation on Twitter network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brahim Dib ◽  
Fahd Kalloubi ◽  
El Habib Nfaoui ◽  
Abdelhak Boulaalam
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Short Term Memory ◽  
Real Life ◽  
Network Graph ◽  
Data Set ◽  
Content Type ◽  
Modeling Strategy ◽  
Searching Method

Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

Knowledge behavior model of e-government social media users

2017 ◽  
Vol 11 (3) ◽  
pp. 456-475 ◽  
Author(s):  
Daphna Shwartz-Asher ◽  
Soon Ae Chun ◽  
Nabil R. Adam
Keyword(s):  
Social Media ◽  
User Behavior ◽  
Methodological Approach ◽  
Behavior Model ◽  
Content Type ◽  
The Public ◽  
Heavy Users ◽  
The Social ◽  
Twitter Users ◽  
The City

Purpose A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting. Design/methodological approach Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations. Findings There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience. Research implications Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively. Originality/value Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

scholarly journals EXPLORATORY DATAANALYSIS AND TOPIC MODELLING ON TED TALKS

2020 ◽  
pp. 1-6
Author(s):  
Parichay Pothepalli
Keyword(s):  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Deep Dive ◽  
Non Profit ◽  
The Public ◽  
Latent Dirichlet Allocation Model ◽  
Exploratory Data ◽  
Business Technology ◽  
Ted Talks

TED (Technology, Entertainment, Design) is a non-profit organization that influences the audience across the globe to deep dive into thinking .The short, powerful talks in more than 100 languages, from great inspired achievers engage the curious people and change their way of perception about issues on science, entertainment, business, technology, global concerns and various other topics. Why do some TED Talks get more views, go viral? What makes a TED talk the change maker in outlook, attitude and behaviour? What intrigues and influences people? This paper aims to analyze the various drivers behind maximum view count of certain TED talks from the start of 2006 till the end of June 2020. The analysis takes into consideration various parameters such as the speaker’s profession, chosen topic, his/her transcript, number of views, comments, tags to name a few. This analysis will help an aspiring TED Talker identify the drivers and plan a video that will attract more view counts. This research paper uses Exploratory Data Analysis with a special emphasis on Natural Language Processing using the native Latent Dirichlet Allocation model from Gensim and the LDA Mallet. Analysis of the TED Talk data suggests that the content is a prime driving factor rather than the public speaking abilities thereby making the view count less predictable.

scholarly journals Renormalization Analysis of Topic Models

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 556
Author(s):  
Sergei Koltcov ◽  
Vera Ignatenko
Keyword(s):  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Optimal Number ◽  
Probabilistic Latent Semantic Analysis ◽  
Model Parameters ◽  
Grid Search ◽  
Renormalization Procedure ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Dirichlet Allocation

In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of topic modeling demonstrates self-similar behavior under variation of the number of clusters. Such behavior allows using a renormalization technique. A combination of renormalization procedure with the Renyi entropy approach allows for quick searching of the optimal number of topics. In this paper, the renormalization procedure is developed for the probabilistic Latent Semantic Analysis (pLSA), and the Latent Dirichlet Allocation model with variational Expectation–Maximization algorithm (VLDA) and the Latent Dirichlet Allocation model with granulated Gibbs sampling procedure (GLDA). The experiments were conducted on two test datasets with a known number of topics in two different languages and on one unlabeled test dataset with an unknown number of topics. The paper shows that the renormalization procedure allows for finding an approximation of the optimal number of topics at least 30 times faster than the grid search without significant loss of quality.

Trending topics and themes in offsite construction(OSC) research

2019 ◽  
Vol 19 (3) ◽  
pp. 343-366 ◽  
Author(s):  
Guiwen Liu ◽  
Juma Hamisi Nzige ◽  
Kaijian Li
Keyword(s):  
Latent Dirichlet Allocation ◽  
Information Modeling ◽  
Funding Agency ◽  
Future Research ◽  
Allocation Model ◽  
Content Type ◽  
Latent Dirichlet Allocation Model ◽  
Similar Information ◽  
Building Information ◽  
The Impact

Purpose The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline. Design/methodology/approach The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition. Findings This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information. Research limitations/implications The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC. Practical implications This study is of values to the scholars, as it could stimulate research to under-researched areas. Originality/value This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.

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.

The effects of illicit financial flows on oil and gas revenue generation in Nigeria

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bello Umar ◽  
Zayyanu Mohammed
Keyword(s):  
Crude Oil ◽  
Oil And Gas ◽  
Academic Research ◽  
Subject Area ◽  
Financial Flows ◽  
Revenue Generation ◽  
Content Type ◽  
The Public ◽  
Illicit Flows ◽  
Illicit Financial Flows

Purpose The purpose of this study is to determine the extent illicit flows affect the oil and gas revenue generation in Nigeria specifically the activities concerning oil theft. Design/methodology/approach A qualitative approach using a systematic quantitative assessment technique was used to select peer-reviewed articles and reports that discussed crude oil theft in Nigeria. This was followed by the use of empirical evidence and content analysis. Findings Crude oil theft in Nigeria accounts for 10% of illicit financial flows (IFFs) from Africa annually and this amounts to US$6bn annually. Research limitations/implications Oil theft is a new subject area of public policy and academic research; data, secondary literature and peer-reviewed journal articles are limited. This paper was from the public sector perspective only. Originality/value This study is one of the few works to highlight the connection between crude oil theft and IFFs.

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