CID: Categorical Influencer Detection on microtext-based social media

2020 ◽  
Vol 44 (5) ◽  
pp. 1027-1055
Author(s):  
Thanh-Tho Quan ◽  
Duc-Trung Mai ◽  
Thanh-Duy Tran
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Learning Approaches ◽  
Content Type ◽  
Modeling Process ◽  
Variational Autoencoder ◽  
Media Channels

PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

Analyzing the startup ecosystem of India: a Twitter analytics perspective

2019 ◽  
Vol 17 (2) ◽  
pp. 262-281 ◽  
Author(s):  
Shiwangi Singh ◽  
Akshay Chauhan ◽  
Sanjay Dhir
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Relevant Information ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Content Type ◽  
Twitter Analytics ◽  
Bayes Algorithm

Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

Deep Learning in Social Media Analysis

2021 ◽  
pp. 164-194
Author(s):  
Vaishali Yogesh Baviskar ◽  
Rachna Yogesh Sable
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Media Analysis ◽  
Network Services ◽  
Social Media Analytics ◽  
Learning Approaches ◽  
Election Result ◽  
The Social ◽  
Social Network Services

Social media analytics keep on collecting the information from different media platforms and then calculating the statistical data. Twitter is one of the social network services which has ample amount of data where many users used post significant amounts of data on a regular basis. Handling such a large amount of data using traditional tools and technologies is very complicated. One of the solutions to this problem is the use of machine learning and deep learning approaches. In this chapter, the authors present a case study showing the use of Twitter data for predicting the election result of the political parties.

Data augmentation using a variational autoencoder for estimating property prices

2021 ◽  
Vol 39 (3) ◽  
pp. 408-418 ◽  
Author(s):  
Changro Lee
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Data Augmentation ◽  
Model Performance ◽  
Content Type ◽  
Original Dataset ◽  
Learning Techniques ◽  
Variational Autoencoder ◽  
Price Estimation

PurposePrior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.Design/methodology/approachThe deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.FindingsThe results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.Originality/valueAlthough deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

Corporate disclosure via social media: a data science approach

2020 ◽  
Vol 44 (1) ◽  
pp. 278-298
Author(s):  
Marian H. Amin ◽  
Ehab K.A. Mohamed ◽  
Ahmed Elragal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Gender Diversity ◽  
Financial Disclosure ◽  
Extensive Literature ◽  
Content Type ◽  
Board Characteristics

Purpose The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information. Design/methodology/approach This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation topic modeling to identify financial disclosure tweets. Panel, Logistic and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics. Findings Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity and board tenure. Originality/value The extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive vs non-executive directors relating to disclosure decisions.

scholarly journals Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

2021 ◽  
Vol 16 (4) ◽  
pp. 1042-1065
Author(s):  
Anne Gottfried ◽  
Caroline Hartmann ◽  
Donald Yates
Keyword(s):  
Data Visualization ◽  
Business Intelligence ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Open Government ◽  
Open Government Data ◽  
Market Opportunities ◽  
Government Data ◽  
Source Of Information

The business intelligence (BI) market has grown at a tremendous rate in the past decade due to technological advancements, big data and the availability of open source content. Despite this growth, the use of open government data (OGD) as a source of information is very limited among the private sector due to a lack of knowledge as to its benefits. Scant evidence on the use of OGD by private organizations suggests that it can lead to the creation of innovative ideas as well as assist in making better informed decisions. Given the benefits but lack of use of OGD to generate business intelligence, we extend research in this area by exploring how OGD can be used to generate business intelligence for the identification of market opportunities and strategy formulation; an area of research that is still in its infancy. Using a two-industry case study approach (footwear and lumber), we use latent Dirichlet allocation (LDA) topic modeling to extract emerging topics in these two industries from OGD, and a data visualization tool (pyLDAVis) to visualize the topics in order to interpret and transform the data into business intelligence. Additionally, we perform an environmental scanning of the environment for the two industries to validate the usability of the information obtained. The results provide evidence that OGD can be a valuable source of information for generating business intelligence and demonstrate how topic modeling and visualization tools can assist organizations in extracting and analyzing information for the identification of market opportunities.

scholarly journals Managing brand presence through social media: the case of UK football clubs

2014 ◽  
Vol 24 (2) ◽  
pp. 181-204 ◽  
Author(s):  
Jeff McCarthy ◽  
Jennifer Rowley ◽  
Catherine Jane Ashworth ◽  
Elke Pioch
Keyword(s):  
Social Media ◽  
Web Sites ◽  
Short Term ◽  
Content Type ◽  
Study Approach ◽  
Football Clubs ◽  
Multiple Case ◽  
Community Growth ◽  
Further Development

Purpose – The purpose of this paper is to contribute knowledge on the issues and benefits associated with managing brand presence and relationships through social media. UK football clubs are big businesses, with committed communities of fans, so are an ideal context from which to develop an understanding of the issues and challenges facing organisations as they seek to protect and promote their brand online. Design/methodology/approach – Due to the emergent nature of social media, and the criticality of the relationships between clubs and their fans, an exploratory study using a multiple case study approach was used to gather rich insights into the phenomenon. Findings – Clubs agreed that further development of social media strategies had potential to deliver interaction and engagement, community growth and belonging, traffic flow to official web sites and commercial gain. However, in developing their social media strategies they had two key concerns. The first concern was the control of the brand presence and image in social media, and how to respond to the opportunities that social media present to fans to impact on the brand. The second concern was how to strike an appropriate balance between strategies that deliver short-term revenue, and those that build longer term brand loyalty. Originality/value – This research is the first to offer insights into the issues facing organisations when developing their social media strategy.

scholarly journals A stock price prediction method based on deep learning technology

2021 ◽  
Vol 5 (1) ◽  
pp. 55-72
Author(s):  
Xuan Ji ◽  
Jiachen Wang ◽  
Zhijun Yan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Stock Price ◽  
Prediction Method ◽  
Series Data ◽  
Learning Technology ◽  
Content Type ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Text Feature

Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

scholarly journals Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation

2019 ◽  
Vol 8 (12) ◽  
pp. 881-885
Keyword(s):  
Online Social Networks ◽  
Topic Modeling ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Good Accuracy ◽  
Ground Truth ◽  
Online Social Media ◽  
Diffusion Dynamics ◽  
Dirichlet Allocation

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings

Adoption strategies of social media in B2B firms: a multiple case study approach

2018 ◽  
Vol 33 (5) ◽  
pp. 730-743 ◽  
Author(s):  
Maryam Lashgari ◽  
Catherine Sutton-Brady ◽  
Klaus Solberg Søilen ◽  
Pernilla Ulfvengren
Keyword(s):  
Social Media ◽  
Multinational Corporations ◽  
Multiple Case Study ◽  
Communication Strategy ◽  
Case Study Approach ◽  
Content Type ◽  
Study Approach ◽  
Study Results ◽  
Multiple Case

PurposeThe purpose of this study is to clarify business-to-business (B2B) firms’ strategies of social media marketing communication. The study aims to explore the factors contributing to the formation and adoption of integration strategies and identify who the B2B firms target.Design/methodology/approachA multiple case study approach is used to compare four multinational corporations and their practices. Face-to-face interviews with key managers, and extensive readings and observations of the firms’ websites and social media platforms have been conducted.FindingsThe study results in a model, illustrating different processes of selection, adoption and integration involved in the development of social media communication strategy for B2B firms. Major factors involved in determining the platform type, and strategies used within different phases and processes are identified.Research limitations/implicationsAs the chosen methodology may limit generalizability, further research is encouraged to test the model within a B2B context especially within small and medium enterprises as only large multinational corporations were investigated in this study.Practical implicationsThe paper provides insight into how B2B marketers can align social media with their firms’ goals through the strategic selection of platforms to reach the targeted audience and communicate their message.Originality/valueThe study uncovers the benefits gained by B2B firms’ through interaction with individuals on social media. This is a significant contribution as the value of such interaction was previously undefined and acted as a barrier for adopting social media in some B2B firms.

Impact of Digital Marketing on ICDL 2016 Event Promotion

2020 ◽  
pp. 216-230
Author(s):  
Reeta Sharma ◽  
P. K. Bhattacharya ◽  
Shantanu Ganguly ◽  
Arun Kumar
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Human Life ◽  
Age Groups ◽  
Digital Marketing ◽  
The World ◽  
Knowledge Economies ◽  
Media Channels ◽  
User Friendly

Today's world is technology-driven. Technology has penetrated almost every sphere of human life. Digital marking is one of the technologies that have attracted people from different age groups all over the world with their advanced nature of applications and uses. One of the foremost reasons why patrons like to use this technology is because these are not only user-friendly in nature and innovativeness but also carry the knowledge economies. Marketing and branding through digital media channels are very decent ventures that have steadily increased in value and are thereby considered safe and secure investments. In this chapter, the authors discuss a case study of ICDL 2016 conference where social media and other technology is widely used to market this event and catch prospective users.

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