Identifying customer knowledge on social media through data analytics

2019 ◽  
Vol 32 (1) ◽  
pp. 152-169 ◽  
Author(s):  
Wu He ◽  
Weidong Zhang ◽  
Xin Tian ◽  
Ran Tao ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Customer Knowledge ◽  
Content Type ◽  
Customer Knowledge Management ◽  
The Social ◽  
Media Data

Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge. Findings The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers. Originality/value This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Gaining competitive intelligence from social media data

2015 ◽  
Vol 115 (9) ◽  
pp. 1622-1636 ◽  
Author(s):  
Wu He ◽  
Jiancheng Shen ◽  
Xin Tian ◽  
Yaohang Li ◽  
Vasudeva Akula ◽  
...  
Keyword(s):  
Social Media ◽  
Business Value ◽  
Competitive Intelligence ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Content Type ◽  
Study Results ◽  
Media Data

Purpose – Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence. Design/methodology/approach – The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015. Findings – The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion. Originality/value – So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

A new approach of social media analytics to predict service quality: evidence from the airline industry

2019 ◽  
Vol 33 (1) ◽  
pp. 51-70 ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Chuanyi Tang ◽  
Ling Li ◽  
Hangjun Xu ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Service Quality ◽  
Sentiment Analysis ◽  
Airline Industry ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Content Type ◽  
Media Data

Purpose Research on how to use social media data to measure and evaluate service quality is still limited. To fill the research gap in the literature, the purpose of this paper is to open a new avenue for future work to measure the service quality in the service industry by developing a new analytical approach of using social media analytics to evaluate service quality. Design/methodology/approach This paper uses social media data to measure the service quality of the airline industry with the SERVQUAL metrics. A novel benchmark data set was created for each SERVQUAL metric. The data set was analyzed through text mining and sentiment analysis. Findings By comparing the results from social media with official service quality report from the Department of Transportation, the authors found that the proposed service quality metrics from social media are valid and can be used to estimate the service quality. Practical implications This paper presents service quality metrics and a methodology that can be easily adopted by other businesses to assess service quality. This study also provides guidance and suggestions to help businesses understand how to collect and analyze social media data for the purpose of evaluating service quality. Originality/value This paper offers a novel methodology that uses text mining and sentiment analysis to help the airline industry assess its service quality.

Destination image through social media analytics and survey method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael S. Lin ◽  
Yun Liang ◽  
Joanne X. Xue ◽  
Bing Pan ◽  
Ashley Schroeder
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Destination Image ◽  
Social Media Analytics ◽  
Tourism Destination ◽  
Social Media Data ◽  
Content Type ◽  
Holistic Understanding ◽  
Intercept Survey ◽  
Media Data

Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

To sell, or not to sell: social media data-breach in second-hand Android devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oussama BenRhouma ◽  
Ali AlZahrani ◽  
Ahmad AlKhodre ◽  
Abdallah Namoun ◽  
Wasim Ahmad Bhat
Keyword(s):  
Social Media ◽  
Social Network ◽  
Use Cases ◽  
Road Accidents ◽  
Data Breach ◽  
Social Media Data ◽  
Content Type ◽  
Private Data ◽  
The Social ◽  
Media Data

Purpose The purpose of this paper is to investigate the private-data pertaining to the interaction of users with social media applications that can be recovered from second-hand Android devices. Design/methodology/approach This study uses a black-box testing-principles based methodology to develop use-cases that simulate real-world case-scenarios of the activities performed by the users on the social media application. The authors executed these use-cases in a controlled experiment and examined the Android smartphone to recover the private-data pertaining to these use-cases. Findings The results suggest that the social media data recovered from Android devices can reveal a complete timeline of activities performed by the user, identify all the videos watched, uploaded, shared and deleted by the user, disclose the username and user-id of the user, unveil the email addresses used by the user to download the application and share the videos with other users and expose the social network of the user on the platform. Forensic investigators may find this data helpful in investigating crimes such as cyber bullying, racism, blasphemy, vehicle thefts, road accidents and so on. However, this data-breach in Android devices is a threat to user's privacy, identity and profiling in second-hand market. Practical implications Perceived notion of data sanitisation as a result of application removal and factory-reset can have serious implications. Though being helpful to forensic investigators, it leaves the user vulnerable to privacy breach, identity theft, profiling and social network revealing in second-hand market. At the same time, users' sensitivity towards data-breach might compel users to refrain from selling their Android devices in second-hand market and hamper device recycling. Originality/value This study attempts to bridge the literature gap in social media data-breach in second-hand Android devices by experimentally determining the extent of the breach. The findings of this study can help digital forensic investigators in solving crimes such as vehicle theft, road accidents, cybercrimes and so on. It can assist smartphone users to decide whether to sell their smartphones in a second-hand market, and at the same time encourage developers and researchers to design methods of social media data sanitisation.

Social media analytics: how they support company public relations

2019 ◽  
Vol 40 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Lisa Tam ◽  
Jeong-Nam Kim
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Design Methodology ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
The Public Sphere ◽  
Use Of Social Media ◽  
Media Data

Purpose In the midst of practitioners’ increasing use of social media analytics (SMA) in guiding public relations (PR) strategy, this paper aims to present the capabilities and limitations of these tools and offers suggestions on how to best use them to gain research-based insights. Design/methodology/approach This review assesses the capabilities and limitations of SMA tools based on industry reports and research articles on trends in PR and SMA. Findings The strengths of SMA tools lie in their capability to gather and aggregate a large quantity of real-time social media data, use algorithms to analyze the data and present the results in ways meaningful to organizations and understand networks of issues and publics. However, there are also challenges, including the increasing restricted access to social media data, the increased use of bots, skewing social conversations in the public sphere, the lack of capability to analyze certain types of data, such as visual data and the discrepancy between data collected on social media and through other methods. Originality/value This review suggests that PR professionals acknowledge the capabilities and limitations of SMA tools when using them to inform strategy.

Strengths and Limitations of Social Media Analytics Tools

2017 ◽  
pp. 198-219 ◽  
Author(s):  
Dražena Gašpar ◽  
Mirela Mabić
Keyword(s):  
Social Media ◽  
Financial Sector ◽  
Point Of View ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Business Goals ◽  
Adoption And Implementation ◽  
The Social ◽  
Tools And Techniques ◽  
Media Data

The aim of this chapter is to research and present strengths and limitations of social media analytics tools used in the financial sector. Emphasis is on the business point of view that sees the social media analytics as a collection of tools that transform semi-structured and unstructured social data into noteworthy business insight. There are two main aspects of social media analytics: the technology aspect which covers identifying, extracting, and analyzing social media data using sophisticated tools and techniques; and the business aspect which interprets the data findings and aligns them with business goals. Namely, it is simply not enough to have a social media analytics tool; the tool should be strategically aligned to support existing business goals. The chapter offers a framework for easier adoption and implementation of these tools in the financial sector.

Application of social media analytics: a case of analyzing online hotel reviews

2017 ◽  
Vol 41 (7) ◽  
pp. 921-935 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Ran Tao ◽  
Weidong Zhang ◽  
Gongjun Yan ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Customer Reviews ◽  
Media Data

Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emilio Pindado ◽  
Ramo Barrena
Keyword(s):  
Social Media ◽  
Clustering Algorithm ◽  
Social Representations ◽  
Local Level ◽  
Geographical Information ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Food Trends ◽  
Media Data

PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

Determining social media impact on the politics of developing countries using social network analytics

2016 ◽  
Vol 50 (4) ◽  
pp. 481-507 ◽  
Author(s):  
Collins Udanor ◽  
Stephen Aneke ◽  
Blessing Ogechi Ogbuokiri
Keyword(s):  
Social Media ◽  
Developing Countries ◽  
Developing Economies ◽  
General Election ◽  
Typical Application ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
The Impact ◽  
Media Data

Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

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