scholarly journals SOCIAL MEDIA DATA AS A USER PERCEPTION TOOL: EVALUATION OF USER EXPERIENCES IN SULTANAHMET AREA

2021 ◽  
Author(s):  
Ezgi GÜLER TOZLUOĞLU ◽  
Caglar TOZLUOGLU ◽  
Dilcan GÜLER ◽  
Mehmet Emre GÜLER
Keyword(s):  
Social Media ◽  
User Perception ◽  
User Experiences ◽  
Social Media Data ◽  
Tool Evaluation ◽  
Media Data

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.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

scholarly journals Missing value or behaviour: how to increase the signal of social media data

METRON ◽  
2021 ◽  
Author(s):  
Paolo Mariani ◽  
Andrea Marletta
Keyword(s):  
Social Media ◽  
Missing Data ◽  
Everyday Life ◽  
Processing Technique ◽  
Missing Value ◽  
Social Media Data ◽  
Practical Strategy ◽  
Specific Behaviour ◽  
Complex Features ◽  
Media Data

AbstractSocial media has become a widespread element of people’s everyday life, which is used to communicate and generate contents. Among the several ways to express a reaction to social media contents, the “Likes” are critical. Indeed, they convey preferences, which drive existing markets or allow the creation of new ones. Nevertheless, the appreciation indicators have some complex features, as for example the interpretation of the absence of “Likes”. In this case, the lack of approval may be considered as a specific behaviour. The present study aimed to define whether the absence of Likes may indicate the presence of a specific behaviour through the contextualization of the treatment of missing data applied to real cases. We provided a practical strategy for extracting more knowledge from social media data, whose synthesis raises several measurement problems. We proposed an approach based on the disambiguation of missing data in two modalities: “Dislike” and “Nothing”. Finally, a data pre-processing technique was suggested to increase the signal of social media data.

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