Using Social Media Data to Plan for Tourism

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.

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Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

IIM Kozhikode Society & Management Review ◽

10.1177/22779752211011810 ◽

2021 ◽

pp. 227797522110118

Author(s):

Amit K. Srivastava ◽

Rajhans Mishra

Keyword(s):

Social Media ◽

Data Quality ◽

Quality Of Data ◽

Social Media Data ◽

National Crisis ◽

Social Media Platforms ◽

Quality Issues ◽

The One ◽

Media Data

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

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Linking Twitter and survey data: asymmetry in quantity and its impact

EPJ Data Science ◽

10.1140/epjds/s13688-021-00286-7 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Tarek Al Baghal ◽

Alexander Wenz ◽

Luke Sloan ◽

Curtis Jessop

Keyword(s):

Social Media ◽

Survey Data ◽

Linked Data ◽

Social Research ◽

Data Sources ◽

Social Media Data ◽

Twitter Data ◽

Unique Source ◽

Methodological Aspects ◽

Media Data

AbstractLinked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.

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New Data Sources in Social Science Research: Things to Know Before Working With Reddit Data

Social Science Computer Review ◽

10.1177/0894439319893305 ◽

2019 ◽

pp. 089443931989330 ◽

Cited By ~ 4

Author(s):

Ashley Amaya ◽

Ruben Bach ◽

Florian Keusch ◽

Frauke Kreuter

Keyword(s):

Social Media ◽

Social Science ◽

Social Science Research ◽

Science Research ◽

Lessons Learned ◽

Data Sources ◽

Front Page ◽

Social Media Data ◽

New Research ◽

Media Data

Social media are becoming more popular as a source of data for social science researchers. These data are plentiful and offer the potential to answer new research questions at smaller geographies and for rarer subpopulations. When deciding whether to use data from social media, it is useful to learn as much as possible about the data and its source. Social media data have properties quite different from those with which many social scientists are used to working, so the assumptions often used to plan and manage a project may no longer hold. For example, social media data are so large that they may not be able to be processed on a single machine; they are in file formats with which many researchers are unfamiliar, and they require a level of data transformation and processing that has rarely been required when using more traditional data sources (e.g., survey data). Unfortunately, this type of information is often not obvious ahead of time as much of this knowledge is gained through word-of-mouth and experience. In this article, we attempt to document several challenges and opportunities encountered when working with Reddit, the self-proclaimed “front page of the Internet” and popular social media site. Specifically, we provide descriptive information about the Reddit site and its users, tips for using organic data from Reddit for social science research, some ideas for conducting a survey on Reddit, and lessons learned in merging survey responses with Reddit posts. While this article is specific to Reddit, researchers may also view it as a list of the type of information one may seek to acquire prior to conducting a project that uses any type of social media data.

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Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Journal Of Big Data ◽

10.1186/s40537-021-00459-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hans Christian ◽

Derwin Suhartono ◽

Andry Chowanda ◽

Kamal Z. Zamli

Keyword(s):

Social Media ◽

Deep Learning ◽

Extraction Method ◽

Language Model ◽

Model Averaging ◽

Data Sources ◽

Online Information ◽

Social Media Data ◽

Personality Prediction ◽

Media Data

AbstractThe ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can give valuable insights on their personalities (e.g., in terms of predicting job satisfaction, specific preferences, as well as the success of professional and romantic relationship) and getting it without the hassle of taking formal personality test. Termed personality prediction, the process involves extracting the digital content into features and mapping it according to a personality model. Owing to its simplicity and proven capability, a well-known personality model, called the big five personality traits, has often been adopted in the literature as the de facto standard for personality assessment. To date, there are many algorithms that can be used to extract embedded contextualized word from textual data for personality prediction system; some of them are based on ensembled model and deep learning. Although useful, existing algorithms such as RNN and LSTM suffers from the following limitations. Firstly, these algorithms take a long time to train the model owing to its sequential inputs. Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. To address these aforementioned limitations, this paper introduces a new prediction using multi model deep learning architecture combined with multiple pre-trained language model such as BERT, RoBERTa, and XLNet as features extraction method on social media data sources. Finally, the system takes the decision based on model averaging to make prediction. Unlike earlier work which adopts a single social media data with open and close vocabulary extraction method, the proposed work uses multiple social media data sources namely Facebook and Twitter and produce a predictive model for each trait using bidirectional context feature combine with extraction method. Our experience with the proposed work has been encouraging as it has outperformed similar existing works in the literature. More precisely, our results achieve a maximum accuracy of 86.2% and 0.912 f1 measure score on the Facebook dataset; 88.5% accuracy and 0.882 f1 measure score on the Twitter dataset.

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StanceVis Prime: visual analysis of sentiment and stance in social media texts

Journal of Visualization ◽

10.1007/s12650-020-00684-5 ◽

2020 ◽

Vol 23 (6) ◽

pp. 1015-1034

Author(s):

Kostiantyn Kucher ◽

Rafael M. Martins ◽

Carita Paradis ◽

Andreas Kerren

Keyword(s):

Social Media ◽

Visual Analytics ◽

Visual Analysis ◽

Research Problem ◽

Data Sources ◽

Data Series ◽

Text Documents ◽

Document Collections ◽

Social Media Data ◽

Media Data

Abstract Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert. Graphic abstract

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Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w2-139-2017 ◽

2017 ◽

Vol IV-4/W2 ◽

pp. 139-146

Author(s):

J. Ajayakumar ◽

E. Shook ◽

V. K. Turner

Keyword(s):

Social Media ◽

Extreme Events ◽

The United States ◽

Environmental Data ◽

Data Sources ◽

Social Media Data ◽

Public Response ◽

Spatio Temporal ◽

Media Data ◽

Media Responses

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

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Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2528-04 ◽

2015 ◽

Vol 2528 (1) ◽

pp. 27-37 ◽

Cited By ~ 9

Author(s):

Abdullah Kurkcu ◽

Ender Faruk Morgul ◽

Kaan Ozbay

Keyword(s):

Social Media ◽

Data Collection ◽

Travel Time ◽

Real Time ◽

Open Data ◽

Data Sources ◽

Incident Management ◽

Virtual Sensors ◽

Social Media Data ◽

Media Data

Open data sources and social media data are gaining increasing attention as important information providers in transportation and incident management. In this paper, practical evidence for the emerging potential of online and open data sources is presented. The authors’ previous research on virtual sensors is combined and extended by integrating real-time incident information and social media network engagement. The fundamental contribution of this paper is the development of an extended virtual sensor framework to provide an automated travel time data collection method as incidents occur. In addition, social media data can be useful for more effective real-time incident response. The proposed framework can easily be modified and used to evaluate travel time effects of incidents on roadways and clearance times and to make use of social media data in obtaining time-critical incident-related information.

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How social media reshapes action on distant customers: some empirical evidence

Accounting Auditing & Accountability Journal ◽

10.1108/aaaj-07-2015-2136 ◽

2017 ◽

Vol 30 (4) ◽

pp. 777-794 ◽

Cited By ~ 16

Author(s):

Deborah Agostino ◽

Yulia Sidorova

Keyword(s):

Social Media ◽

Joint Control ◽

Control Activity ◽

Social Media Data ◽

Content Type ◽

Corporate Website ◽

Social Media Platforms ◽

The One ◽

Centres Of Calculation ◽

Media Data

Purpose The purpose of this paper is to investigate how centres of calculation, now emerging in connection with social media, impact on the process of acting on distant customers. Specifically, the authors are interested in exploring how the distance between the organization and its customer is affected and how knowledge is accumulated within this centre. Design/methodology/approach A case study in an Italian telecommunication company was conducted over a time horizon of two years, analysing data sources in the form of interviews, documents and reports, corporate website, social media platforms and participants’ observations. With the adoption of social media, the company configured a new centre of calculation, called monitoring room, in the attempt to accumulate knowledge about its customers. The authors unpacked the activity of the centre of calculation discussing its ability to perform action upon a distant periphery and the process of knowledge accumulation inside the centre itself. Findings The results highlight the implication of social media for “action at a distance”. On the one hand, social media blurs the distinction between the centre and a periphery giving rise to a de-centring, and stimulating a joint control activity between the customer and the organization. On the other hand, social media was found vulnerable in providing a unique knowledge about customers: accumulation cycles that exploit social media data can be replicated by users with skills in data analytics and the knowledge they provide might conflict with knowledge provided by traditional data. Originality/value The authors contribute to an emergent stream of literature that is investigating accounting implications derived from social media, by underlying the controversial effects connected with centres of calculation enacted by social media data. The authors suggest that, while social media data provide the organization with huge amount of information real time, at the same time, it contributes to de-centring allowing customers and external actors to act upon the organization, rather than improving knowledge inside the centre.

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

British Food Journal ◽

10.1108/bfj-03-2020-0192 ◽

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.

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