scholarly journals The scale effect of landscape variables on landscape experiences: A multi-scale spatial analysis of social media data in an urban nature park context

2021 ◽  
Author(s):  
Ping Chang ◽  
Anton Stahl Olafsson
Keyword(s):  
Social Media ◽  
Scale Effects ◽  
Relative Magnitude ◽  
Social Media Data ◽  
Landscape Variables ◽  
Recreational Services ◽  
Nature Park ◽  
Nature Parks ◽  
The Relationship ◽  
Media Data

Abstract Context The roles of landscape variables with regard to the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships of landscape features and attributes to categorized nature experiences have not been adequately studied from an experimental perspective. Objectives This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: 1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and what landscape variables are associated with specific dimensions of experience? 2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences? Methods Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulting from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences. Results All considered landscape variables were identified as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context- and place-specific relationships and the limited applicability of simple approaches that assume relationships to be spatially stationary. Conclusions The spatial effect of landscape variables on landscape experiences was clarified and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.

scholarly journals Contrapuntal Connectedness: Analysing Relations Between Social Media Data and Ethnography in Digital Migration Studies

2021 ◽  
pp. 53-85
Author(s):  
Marie Sandberg ◽  
Nina Grønlykke Mollerup ◽  
Luca Rossi
Keyword(s):  
Social Media ◽  
Syrian Refugees ◽  
Migration Studies ◽  
Social Data ◽  
Social Media Data ◽  
Ethnographic Fieldwork ◽  
Migration Research ◽  
Facebook Pages ◽  
The Relationship ◽  
Media Data

AbstractThis chapter presents a rethinking of the relationship between ethnography and so-called big social data as being comparable to those between a sum and its parts (Strathern 1991/2004). Taking cue from Tim Ingold’s one world anthropology (2018) the chapter argues that relations between ethnography and social media data can be established as contrapuntal. That is, the types of material are understood as different, yet fundamentally interconnected. The chapter explores and qualifies this affinity with the aim of identifying potentials and further questions for digital migration research. The chapter is based on ethnographic fieldwork carried out with Syrian refugees and solidarians in the Danish–Swedish borderlands in 2018–2019 as well as data collected for 2011–2018 from 200 public Facebook pages run by solidarity organisations, NGOs, and informal refugee welcome and solidarity groups.

scholarly journals Success in an Online Giving Day: The Role of Social Media in Fundraising

2019 ◽  
Vol 49 (1) ◽  
pp. 74-92 ◽  
Author(s):  
Abhishek Bhati ◽  
Diarmuid McDonnell
Keyword(s):  
Social Media ◽  
Organizational Factors ◽  
Network Size ◽  
Audience Engagement ◽  
Social Media Data ◽  
Size Number ◽  
Social Media Platforms ◽  
The Relationship ◽  
Media Data

Social media platforms offer nonprofits considerable potential for crafting, supporting, and executing successful fundraising campaigns. How impactful are attempts by these organizations to utilize social media to support fundraising activities associated with online Giving Days? We address this question by testing a number of hypotheses of the effectiveness of using Facebook for fundraising purposes by all 704 nonprofits participating in Omaha Gives 2015. Using linked administrative and social media data, we find that fundraising success—as measured by the number of donors and value of donations—is positively associated with a nonprofit’s Facebook network size (number of likes), activity (number of posts), and audience engagement (number of shares), as well as net effects of organizational factors including budget size, age, and program service area. These results provide important new empirical insights into the relationship between social media utilization and fundraising success of nonprofits.

Social Media Relationships

2021 ◽  
Author(s):  
Umoloyouvwe Ejiro Onomake
Keyword(s):  
Social Media ◽  
Participant Observation ◽  
Multiple Relationships ◽  
Social Media Data ◽  
For Profit ◽  
Social Media Platforms ◽  
Media Research ◽  
Use Of Social Media ◽  
The Relationship ◽  
Media Data

Ethnography has been used to research various people and topics online, primarily using netnography and digital ethnography. Researchers and businesses employ digital ethnographic methods to access an assortment of social media platforms in order to learn about social media users. Researchers seek to understand relationships between social media users and organizations from both academic and practitioner perspectives. These organizations run the gamut from for-profit businesses, to nonprofits, nongovernmental organizations (NGOs), and government agencies. The specific focus here is on social media research as it relates to businesses. Organizations make use of social media in a variety of ways, but chiefly to market to clients and to gather information on followers; the latter of which, in turn, helps them understand their target markets. While this social media data is both quantitative and qualitative in nature, the emphasis here centers on qualitative data, particularly the ways businesses interact with social media users. While some firms mainly use older forms of one-way marketing that solely focus on disseminating information, other firms increasingly seek ways to interact with customers and co-create products with clients. Additionally, social media users are creating their own communities, formed due to a shared interest in a brand. Companies strive to learn more about their customers through these groups. Influencers also play a role in the relationship between organizations and social media users by linking their own followerships to products and brands. In turn, influencers develop their own relationships with organizations through sponsorships, thus becoming brands themselves. Influencers risk losing their followerships when followers perceive them as no longer accessible or authentic. This change in perception can occur for a variety of reasons, including when followers believe that an influencer has prioritized brand alignment over building connections with followers. Due to multiple relationships with different brands and their followers, influencers must negotiate the ambiguity and evolving nature of their role. As social media and digital spaces develop, so must the tools used by anthropologists. Anthropologists should remain open to incorporating hallmarks of ethnographic research such as fieldnotes, participant observation, and focus groups in new ways and alongside tools from other disciplines, including market and UX (user experience) research. The divide between practitioners and academics is blurring. Anthropologists can solve client issues while contributing their voices to larger anthropological and societal discussions.

scholarly journals Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook

2015 ◽  
Vol 109 (1) ◽  
pp. 62-78 ◽  
Author(s):  
ROBERT BOND ◽  
SOLOMON MESSING
Keyword(s):  
Social Media ◽  
Social Relationships ◽  
Political Behavior ◽  
Revealed Preferences ◽  
Social Media Data ◽  
Individual Level ◽  
Political Views ◽  
Scaling Process ◽  
The Relationship ◽  
Media Data

We demonstrate that social media data represent a useful resource for testing models of legislative and individual-level political behavior and attitudes. First, we develop a model to estimate the ideology of politicians and their supporters using social media data on individual citizens’ endorsements of political figures. Our measure allows us to place politicians and more than 6 million citizens who are active in social media on the same metric. We validate the ideological estimates that result from the scaling process by showing they correlate highly with existing measures of ideology from Congress, and with individual-level self-reported political views. Finally, we use these measures to study the relationship between ideology and age, social relationships and ideology, and the relationship between friend ideology and turnout.

scholarly journals Modeling the Probability of Fraud in Social Media in a National Cannabis Survey

2021 ◽  
Author(s):  
Lauren M. Dutra ◽  
Matthew C. Farrelly ◽  
Brian Bradfield ◽  
Jamie Ridenhour ◽  
Jamie Guillory
Keyword(s):  
Social Media ◽  
The United States ◽  
Complex Data ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
National Surveys ◽  
Problem Solve ◽  
Data Collections ◽  
The Relationship ◽  
Media Data

Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.

scholarly journals Review of the Application of Social Media Data in Disaster Research

2020 ◽  
Author(s):  
Jiting Tang ◽  
Saini Yang ◽  
Weiping Wang
Keyword(s):  
Social Media ◽  
Hazard Identification ◽  
Research Needs ◽  
Disaster Research ◽  
Social Media Data ◽  
Emergency Rescue ◽  
Data Source ◽  
The Relationship ◽  
Media Data ◽  
Data Sensitivity

Social media data (SMD) is a new data source in disaster research, which can be used in hazard identification, disaster analysis, risk assessment and emergency rescue. This data-driven disaster research needs to find an appropriate method considering the aspect of data sensitivity. So far, the research in this area is focused on the types of hazard, but rarely considers the relationship between the technical methods and applicable tasks. By emphasizing data and method dependencies, we have attempted to summarize the characteristics of SMD in disaster research, viz., “sociality, rapidity, subjectivity, and un-authenticity”, and explore the processing methods in the applications of disaster management. Our work provides ideas and reference to the researchers working in this area from the perspectives of data and research goals.

An Analysis of User Experience of Smartphone based on Product Smartness utilizing Social Media Data

2016 ◽  
Vol 60 (1) ◽  
pp. 1198-1199 ◽  
Author(s):  
Ilsun Rhiu ◽  
Sung Hee Ahn ◽  
Donggun Park ◽  
Wonjoon Kim ◽  
Myung Hwan Yun
Keyword(s):  
Social Media ◽  
User Experience ◽  
User Experiences ◽  
Negative Experiences ◽  
Social Media Data ◽  
Twitter Data ◽  
Smart Products ◽  
Social Metrics ◽  
The Relationship ◽  
Media Data

With rapid technology advancement and an expanding product domain, the definition of smart products has slightly varied (Rijsdijk & Hultink, 2009; Zaeh, Reinhart, Ostgathe, Geiger, & Lau, 2010). Also, from previous studies (Freudenthal & Mook, 2003; Rijsdijk & Hultink, 2003, 2009; Park & Lee, 2014), the relationship between product smartness and consumer appreciations or values can be identified. However, it is unclear to understand implicit needs of the consumers through conducting questionnaire based survey method. This method does not often provide sufficient information on the underlying meaning of the data, and strong evidences of causation to an answer (Gable, 1994). Hence, it could be more effective to collect unrefined and numerous user experiences, which are freely expressed in their own words, for better observation of natural user behaviors. Therefore, we tried to observe user experiences utilizing social media data, which can infer people’s opinions, both at an individual level as well as in aggregate, regarding potentially any subject or event (Schonfeld, 2009), to identify perceived product smartness. Since a smartphone is one of the most successful smart products, it could be represent the characteristics of smart products better than other products. Thus, ‘smart phone’ and ‘mobile phone’ are selected as search keywords. Through literature reviews, the dimensions and attributes related to product smartness from various previous studies were collected. Then, the collected dimensions of product smartness were re-categorized into five main dimensions as follow: ‘Autonomy’, ‘Adaptability’, ‘Multi-functionality’, ‘Connectivity’, and ‘Personalization’. The overall procedure of analyzing the relationship between perceived product smartness and collected user experiences of smart products from external data source (Twitter) is as follow. First, user experience of smart products was collected through mining Twitter data using software tool (SOCIAL metrics). SOCIAL metrics ( http://socialmetrics.co.kr ), which is developed by DaumSoft, can help for analyzing big data. It enables to collect Twitter data and show the frequency of keywords related to user’s search keyword. Second, data pre-processing was conducted. In the search results, the tweets which are not related to user experiences of smart products are eliminated. Third, collected user experiences were categorized according to the conceptual model of product smartness. Then, identifying the relationship between each dimension of product smartness and users’ positive/negative experiences was performed by manually. Finally, the reason of users’ positive or negative emotions on experiences of smart products was identified. A total of 19,288 tweets including ‘smartphone’ were collected from 2014.06.01 ~ 2014.08.31. Among them, a total of 699 tweets are actually related to user experiences of smartphones. The collected tweets were categorized according to the dimension of product smartness and the reason of user’s emotion. According to the results, there were many positive experiences for all of dimensions, but there were negative experiences only for multi-functionality and connectivity. Some results were supported by existing studies. The reason for positive experience on autonomy corresponded with the result of other study that productive daily life is a critical means for users to develop sense of confidence (Jung, 2014). Negative experience of autonomous was not shown in the results, but actually autonomous product does not always increase satisfaction of product. According to Rijsdijk and Hultink (2003), high complexity in using products would decrease satisfaction of products. Providing an autonomous product with indicators that inform the user about what the product is doing may reduce risk perceptions (Rijsdijk & Hultink, 2009). The study suggested that a mining technique can be used to gather and analyze user experience effectively and quantitatively without bias. It is expected that the proposed method could be helpful for understanding user’s implicit needs on the products.

Sign in / Sign up

Export Citation Format

Share Document