Research opportunities at the intersection of social media and survey data

2016 ◽  
Vol 9 ◽  
pp. 67-71 ◽  
Author(s):  
Emma S Spiro
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Research Opportunities

scholarly journals #ODU2ODU: Testing the Benefits of a Partnership Between Sport Management Classrooms

2020 ◽  
Vol 1 ◽  
pp. 18-35
Author(s):  
Brendan O'Hallarn ◽  
James Strode
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Real World ◽  
Sport Management ◽  
Media Use ◽  
Academic Research ◽  
Perceived Benefits ◽  
Media Technologies ◽  
Social Media Site ◽  
The Social

As sport management pedagogy has evolved, an effort has been made to incorporate popular and innovative social media technologies into classroom instruction. Academic research has suggested how the technology can be utilized to provide real-world skills for students and develop proficiencies in an area where many sport management graduates find employment. Notable among the recommendations about social media use by sport management scholars is a lack of research testing the efficacy of these tools in improving curricula. The current study relied on the recommendations of Sanderson and Browning (2015) to use the social media site Twitter to create online partnerships, testing the perceived benefits of such an arrangement through end-of-semester surveys with student participants. While the survey data show a true partnership may be difficult to realize—particularly during a single semester—the benefits of such an assignment were clearly articulated.

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.

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

2019 ◽  
Vol 38 (5) ◽  
pp. 633-650 ◽  
Author(s):  
Josh Pasek ◽  
Colleen A. McClain ◽  
Frank Newport ◽  
Stephanie Marken
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Large Scale ◽  
Presidential Approval ◽  
Social Phenomena ◽  
Social Media Data ◽  
Complex Picture ◽  
Large Scale Survey ◽  
Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

scholarly journals Capturing Associations and Sustainable Competitiveness of Brands from Social Tags

2019 ◽  
Vol 11 (6) ◽  
pp. 1529 ◽  
Author(s):  
Xuan Gong ◽  
Yunchan Zhu ◽  
Rizwan Ali ◽  
Ruijin Guo
Keyword(s):  
Social Media ◽  
Real Time ◽  
Survey Data ◽  
Automobile Industry ◽  
Traditional Method ◽  
Predictive Power ◽  
Determination Method ◽  
Brand Associations ◽  
Social Tags ◽  
Brand Competitiveness

With the explosion of social media, consumers’ minds have become important assets in brand competitions. Determining a brand’s competitive structure based on consumers’ desires is particularly important to effectively establish a brand and maintain sustainable competitiveness. The traditional methods of determining brand competitiveness are costly and time-consuming. In this study, we propose an efficient, systematical, highly automated, and real-time method to determine brand competitiveness based on consumers’ brand associations with the brand’s social tags. Using a set of 45 brands in the automobile industry and around 50,000 social tags, we compared our brand competitiveness determination method with data provided by Interbrand and directly elicited survey data, finding a significant correlation and a better predictive power in consumers’ perceived brand competitiveness than the traditional method. Our proposed method enables managers to create and maintain sustainable brand advantages in consumers’ minds.

Social Networking Sites

2016 ◽  
Vol 12 (4) ◽  
pp. 62-75
Author(s):  
Lydia Kyei-Blankson ◽  
Kamakshi S. Iyer ◽  
Lavanya Subramanian
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Social Networking ◽  
Survey Data ◽  
Social Networking Sites ◽  
Relationship Status ◽  
Web Based ◽  
Patterns Of Use ◽  
Midwestern University ◽  
Use Of Social Media

Social Networking Sites (SNSs) are web-based facilities that allow for social interaction, sharing, communication and collaboration in today's world. In the current study, patterns of use of social media among students at a public Midwestern university are examined. In addition, students were surveyed regarding concerns for privacy and trust and whether concerns differed by gender, ethnicity, employment and relationship status. The survey data gathered from students suggest that students mostly used SNSs from less than one hour to about 3 hours a day and for communication and maintaining relationships. Students also had academic uses for SNSs. Even though concerns for privacy and trust exist, they did not differ by gender, employment and relationship status and students are still willing to use SNSs. The findings from this research have implications for various stakeholders especially instructors who may be considering the use of SNS for academic purposes.

scholarly journals Are people incidentally exposed to news on social media? A comparative analysis

2017 ◽  
Vol 20 (7) ◽  
pp. 2450-2468 ◽  
Author(s):  
Richard Fletcher ◽  
Rasmus Kleis Nielsen
Keyword(s):  
United States ◽  
Social Media ◽  
United Kingdom ◽  
Comparative Analysis ◽  
Survey Data ◽  
Online Survey ◽  
Online News ◽  
News Sources ◽  
Incidental Exposure ◽  
Media Environments

Scholars have questioned the potential for incidental exposure in high-choice media environments. We use online survey data to examine incidental exposure to news on social media (Facebook, YouTube, Twitter) in four countries (Italy, Australia, United Kingdom, United States). Leaving aside those who say they intentionally use social media for news, we compare the number of online news sources used by social media users who do not see it as a news platform, but may come across news while using it (the incidentally exposed), with people who do not use social media at all (non-users). We find that (a) the incidentally exposed users use significantly more online news sources than non-users, (b) the effect of incidental exposure is stronger for younger people and those with low interest in news and (c) stronger for users of YouTube and Twitter than for users of Facebook.

scholarly journals Sentiment Analysis of Social Survey Data for Local City Councils

2022 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Marianna Lepelaar ◽  
Adam Wahby ◽  
Martha Rossouw ◽  
Linda Nikitin ◽  
Kanewa Tibble ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Survey Data ◽  
Smart Cities ◽  
Social Indicators ◽  
Media Analysis ◽  
Social Surveys ◽  
Analysis Techniques ◽  
The Social ◽  
The City

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

scholarly journals Predictive modeling to study lifestyle politics with Facebook likes

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Stiene Praet ◽  
Peter Van Aelst ◽  
Patrick van Erkel ◽  
Stephan Van der Veeken ◽  
David Martens
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Party System ◽  
Political Polarization ◽  
Daily Lives ◽  
Political Preference ◽  
Social Media Data ◽  
Cultural Preferences ◽  
Wide Range ◽  
Media Data

Abstract“Lifestyle politics” suggests that political and ideological opinions are strongly connected to our consumption choices, music and food taste, cultural preferences, and other aspects of our daily lives. With the growing political polarization this idea has become all the more relevant to a wide range of social scientists. Empirical research in this domain, however, is confronted with an impractical challenge; this type of detailed information on people’s lifestyle is very difficult to operationalize, and extremely time consuming and costly to query in a survey. A potential valuable alternative data source to capture these values and lifestyle choices is social media data. In this study, we explore the value of Facebook “like” data to complement traditional survey data to study lifestyle politics. We collect a unique dataset of Facebook likes and survey data of more than 6500 participants in Belgium, a fragmented multi-party system. Based on both types of data, we infer the political and ideological preference of our respondents. The results indicate that non-political Facebook likes are indicative of political preference and are useful to describe voters in terms of common interests, cultural preferences, and lifestyle features. This shows that social media data can be a valuable complement to traditional survey data to study lifestyle politics.

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