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.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

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.

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.

Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

2016 ◽  
Vol 40 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Yoosin Kim ◽  
Rahul Dwivedi ◽  
Jie Zhang ◽  
Seung Ryul Jeong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Purchase Intention ◽  
Opinion Mining ◽  
Sales Performance ◽  
Competitive Intelligence ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Media Data

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

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.

scholarly journals Network based model of social media big data predicts contagious disease diffusion

2017 ◽  
Vol 45 (3) ◽  
pp. 110-120 ◽  
Author(s):  
Lauren S. Elkin ◽  
Kamil Topal ◽  
Gurkan Bebek
Keyword(s):  
Social Media ◽  
Big Data ◽  
Disease Outbreaks ◽  
Social Media Data ◽  
Content Type ◽  
Contagious Diseases ◽  
The Social ◽  
The Usa ◽  
Disease Diffusion ◽  
Media Data

Purpose Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks. Design/methodology/approach In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data. Findings The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets. Originality/value First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

scholarly journals Using Social Media Data to Plan for Tourism

2020 ◽  
Vol 39 (3) ◽  
pp. 125-138
Author(s):  
Alina Zajadacz ◽  
Aleksandra Minkwitz
Keyword(s):  
Social Media ◽  
Local Level ◽  
Data Sources ◽  
Communication Tool ◽  
Tourism Planning ◽  
Communication Tools ◽  
Social Media Data ◽  
Planning Methods ◽  
The One ◽  
Media Data

AbstractThe purpose of the article is to present the concept of using social media (SM) as data sources and communication tools, useful at the various stages of planning, implementing and monitoring the effects of tourism development on a local level. The first part discusses the stages of planning, then presents the characteristics of SM, along with a discussion of the issues presented in the literature to this date. The next part presents data sources and methods of research on SM and functions that they can perform in tourism. The concept presented, on the one hand, reviews the perspectives of practical use of SM as a communication tool and source of data and, on the other hand, the challenges related to the need to further deepen research on tourism planning methods that are adequate to the continuously changing environment.

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.

Determining factors of participants' attitudes toward the ethics of social media data research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Chen ◽  
Chuanfu Chen ◽  
Si Li
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Ethical Issues ◽  
Equation Modeling ◽  
Research Subjects ◽  
Social Media Data ◽  
Content Type ◽  
Mediating Role ◽  
Determining Factors ◽  
Media Data

PurposeThe purpose of this study was to investigate the participants' attitudes toward the ethical issues caused by collecting social media data (SMD) for research, as well as the effects of familiarity, trust and altruism on the participants' attitudes toward the ethics of SMD research. It is hoped that through this study, scholars will be reminded to respect participants and engage in ethical reflection when using SMD in research.Design/methodology/approachThis study adopted social media users as its research subjects and used Sina Microblog, the world's largest Chinese social media platform, as the example. Based on the 320 valid responses collected from a survey, structural equation modeling was employed to examine the research model.FindingsThe results indicated that altruism, familiarity and trust have significant influences on participants' attitudes toward the ethics of SMD research, and familiarity also influences attitudes through the mediating role of trust and altruism.Originality/valueThis study explored the mechanism underlying the relationship between the determining factors and participants' attitudes toward the ethics of SMD research, and the results demonstrated that the informed consent mechanism is an effective way to communicate with participants and that the guiding responsibility of the platform should be improved to standardize SMD research.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

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