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Article
Publication date: 3 October 2018

Hong-liang Sun, Eugene Ch’ng and Simon See

The purpose of this paper is to investigate political influential spreaders in Twitter at the juncture before and after the Malaysian General Election in 2013 (MGE2013…

Abstract

Purpose

The purpose of this paper is to investigate political influential spreaders in Twitter at the juncture before and after the Malaysian General Election in 2013 (MGE2013) for the purpose of understanding if the political sphere within Twitter reflects the intentions, popularity and influence of political figures in the year in which Malaysia has its first “social media election.”

Design/methodology/approach

A Big Data approach was used for acquiring a series of longitudinal data sets during the election period. The work differs from existing methods focusing on the general statistics of the number of followers, supporters, sentiment analysis, etc. A retweeting network has been extracted from tweets and retweets and has been mapped to a novel information flow and propagation network we developed. The authors conducted quantitative studies using k-shell decomposition, which enables the construction of a quantitative Twitter political propagation sphere where members posited at the core areas are more influential than those in the outer circles and periphery.

Findings

The authors conducted a comparative study of the influential members of Twitter political propagation sphere on the election day and the day after. The authors found that representatives of political parties which are located at the center of the propagation network are winners of the presidential election. This may indicate that influential power within Twitter is positively related to the final election results, at least in MGE2013. Furthermore, a number of non-politicians located at the center of the propagation network also significantly influenced the election.

Research limitations/implications

This research is based on a large electoral campaign in a specific election period, and within a predefined nation. While the result is significant and meaningful, more case studies are needed for generalized application for identifying potential winning candidates in future social-media fueled political elections.

Practical implications

The authors presented a simple yet effective model for identifying influential spreaders in the Twitter political sphere. The application of the authors’ approach yielded the conclusion that online “coreness” score has significant influence to the final offline electoral results. This presents great opportunities for applying the novel methodology in the upcoming Malaysian General Election in 2018. The discovery presented here can be used for understanding how different players of political parties engage themselves in the election game in Twitter. The approach can also be adopted as a factor of influence for offline electoral activities. The conception of a quantitative approach in electoral results greatly influenced by social media means that comparative studies could be made in future elections.

Originality/value

Existing works related to general elections of various nations have either bypassed or ignored the subtle links between online and offline influential propagations. The modeling of influence from social media using a longitudinal and multilayered approach is also rarely studied. This simple yet effective method provides a new perspective of practice for understanding how different players behave and mutually shape each other over time in the election game.

Details

Industrial Management & Data Systems, vol. 119 no. 1
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 8 January 2019

Eugene Ch’ng, Shengdan Cai, Tong Evelyn Zhang and Fui-Theng Leow

The purpose of this paper is to present the rationale for democratising the digital reproduction of cultural heritage via “mass photogrammetry”, by providing approaches to…

Abstract

Purpose

The purpose of this paper is to present the rationale for democratising the digital reproduction of cultural heritage via “mass photogrammetry”, by providing approaches to digitise objects from cultural heritage collections housed in museums or private spaces using devices and photogrammetry techniques accessible to the public. The paper is intended as a democratised approach rather than as a “scientific approach” for the purpose that mass photogrammetry can be achieved at scale.

Design/methodology/approach

The methodology aims to convert the art of photogrammetry into a more mechanical approach by overcoming common difficulties faced within exhibition spaces. This approach is replicable and allows anyone possessing inexpensive equipment with basic knowledge of photogrammetry to achieve acceptable results.

Findings

The authors present the experience of acquiring over 300 3D models through photogrammetry from over 25 priority sites and museums in East Asia. The approach covers the entire process from capturing to editing, and importing 3D models into integrated development environments for displays such as interactive 3D, Virtual Reality and Augmented Reality.

Practical implications

The simplistic approach for democratised, mass photogrammetry has implications for stirring public interests in the digital preservation of heritage objects in countries where museums and cultural institutions have little access to digital teams, provided that Intellectual Property issues are cared for. The approach to mass photogrammetry also means that personal cultural heritage objects hidden within the homes of various societies and relics in circulation in the antiques market can be made accessible globally at scale.

Originality/value

This paper focuses on the complete practical nature of photogrammetry conducted within cultural institutions. The authors provide a means for the public to conduct good photogrammetry so that all cultural heritage objects can be digitally recorded and shared globally so as to promote the cross-cultural appreciation of material cultures from the past.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. 9 no. 1
Type: Research Article
ISSN: 2044-1266

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Article
Publication date: 5 September 2018

Mengdi Li, Eugene Ch’ng, Alain Yee Loong Chong and Simon See

Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have…

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Abstract

Purpose

Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments.

Design/methodology/approach

The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox.

Findings

The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions.

Originality/value

This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies.

Details

Industrial Management & Data Systems, vol. 118 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 19 October 2015

Eugene Ch'ng

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal…

Abstract

Purpose

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

Design/methodology/approach

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

Findings

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

Research limitations/implications

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

Practical implications

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

Originality/value

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 11 May 2015

Eugene Ch'ng

The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure…

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913

Abstract

Purpose

The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities.

Design/methodology/approach

The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal “social information landscape”. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos.

Findings

The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this paper using social networks approaches also reveals that communities are formed and maintained from the bottom-up.

Research limitations/implications

The research data is based on a particular data set which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the data set presents a “laboratory” case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved.

Practical implications

The paper showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area – and this will surely be applied and developed further by the computer science and security community.

Originality/value

The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous data sets that have high volume and velocity. Such data sets are therefore unexploited with regards to cyber-community research.

Details

Industrial Management & Data Systems, vol. 115 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 4 April 2016

Alain Yee Loong Chong, Boying Li, Eric W.T. Ngai, Eugene Ch'ng and Filbert Lee

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments…

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8081

Abstract

Purpose

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

Design/methodology/approach

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

Findings

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

Originality/value

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

Details

International Journal of Operations & Production Management, vol. 36 no. 4
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 4 September 2019

Mengdi Li, Eugene Chng, Alain Yee Loong Chong and Simon See

Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However…

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1008

Abstract

Purpose

Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However, research in Emojis is rarely to be seen due to the lack of data at a greater scale. The purpose of this paper is to systematically analyse and compare the usage of Emojis in a cross-cultural manner.

Design/methodology/approach

This research conducted an empirical analysis using a large-scale, cross-regional emoji usage data set from Twitter, a platform where the limited 140 characters allowance has made it essential for the inclusion of emojis within tweets. The extremely large textual data set covers a period of only two months, but the 673m tweets authored by more than 2,081,542 unique users is a sufficiently large sample for the authors to yield significant results.

Findings

This research discovered that the categories and frequencies of Emojis communicated by users can provide a rich source of data to understand cultural differences between Twitter users from a large range of demographics. This research subsequently demonstrated the preferential use of Emojis complies with Hofstede’s Cultural Dimensions Model, in which different representations of demographics and culture within countries present significantly different use of Emojis to communicate emotions.

Originality/value

This study provides a robust example of how to strategically conduct research using large-scale emoji data to pursue research questions previously difficult. To the best of authors’ knowledge, the present study pioneers the first systematic analysis and comparison of the usage of emojis on Twitter across different cultures; it is the largest, in terms of the scale study of emoji usage to-date.

Details

Industrial Management & Data Systems, vol. 119 no. 8
Type: Research Article
ISSN: 0263-5577

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