User-generated Videos

Design students need to explore the problem domain in order to respond to a design problem. Users’ disability or health problems, distant cultures or geographies, or time constraints may prevent students from having direct access to users or the usage contexts. Under those circumstances, students were observed that they consulted user-generated online videos such as YouTube videos, and that they employed role-playing techniques to have subjective embodied experiences and a deeper understanding of the problem. Within the scope of this study, a method for the exploratory research in the early phases of the design process was developed to be employed by design tutors and students when direct contact with users or the usage context is not a viable option. The proposed method brings together user-generated video content analysis techniques with role-playing techniques.Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Industrial Design

The existing literature highlights social network sites (SNS) as important channels for consuming news and receiving misinformation. However, people surf multiple SNS daily, such as Facebook, Twitter, Line, etc. People also connect and interact with each other, given the characteristics of specific SNS. Driven by partisan bias, partisan supporters may be driven to converge on a specific SNS due to a political echo chamber that exists there. In such a partisan convergence scenario, politicized SNS usage may lead to bias to color the spread of fact-checking and, therefore, politically bias SNS users’ level of media literacy. We examine this hypothesis in a pre-registered national survey (n = 1060) in the 2020 Taiwan Presidential Election. Results show that (1), in general, the usage of the private messaging app Line is related to a lower level of media literacy. The effect holds across political and non-political misinformation items. (2) DPP supporters are much more likely to use Facebook than other supporters, while their political opponents are alienated from Facebook. Therefore, (3) Facebook usage is related to a higher media literacy discerning DPP-related fake news but not others, and the effect exists beyond DPP supporters.

Dispersal of COVID-19 related information from YouTube plays a vital role in containing the pandemic and diminishes associated anxiety in the population. This study investigated the characteristics of popular YouTube videos related to COVID-19 outbreak. YouTube videos were searched using the COVID-19 related specific keywords with the eligibility of at least 1 million views. The videos were classified as types of videos (News, TV Shows, Educational, and Documentary) and sentiment-based titled videos (Positive and Negative). Total viewership, length of videos, comments, likes, subscribers of the channel, and the number of days since upload was recorded as video and user engagement characteristics. A total of 93 videos were found eligible to include in the study. About 44.1% of videos were educational and 32.3% of videos were news updates related to COVID-19. Furthermore, 45.7% of videos had a positive sentiment-based title whereas 53.2% of videos had a negative sentiment-based title....

Dispersal of COVID-19 related information from YouTube plays a vital role in containing the pandemic and diminishes associated anxiety in the population. This study investigated the characteristics of popular YouTube videos related to COVID-19 outbreak. YouTube videos were searched using the COVID-19 related specific keywords with the eligibility of at least 1 million views. The videos were classified as types of videos (News, TV Shows, Educational, and Documentary) and sentiment-based titled videos (Positive and Negative). Total viewership, length of videos, comments, likes, subscribers of the channel, and the number of days since upload was recorded as video and user engagement characteristics. A total of 93 videos were found eligible to include in the study. About 44.1% of videos were educational and 32.3% of videos were news updates related to COVID-19. Furthermore, 45.7% of videos had a positive sentiment-based title whereas 53.2% of videos had a negative sentiment-based title....

This paper presents the development of a generative toolkit which utilizes user-generated online videos (UGVs) as found data and repurposes them for exploratory design research and idea generation. The toolkit aims at supporting design students to browse, select and analyse UGVs for generating design insights and ideas for unfamiliar problem domains and hard-to-access user groups. The toolkit development process started with a retrospective analysis of a selection of industrial design studio projects followed by interviews with the students and the tutors involved. An initial model was developed and used in a 5-week design studio project taken by 75 senior year industrial design students at Anadolu University. Based upon the findings of these studies, the revised toolkit was implemented in a half-day crash workshop involving a small number of participants. During the workshop, the participants were provided with a concise literature review regarding a specific design project topic, a YouTube playlist of related videos, the video analysis board samples from previous studies, and the video analysis board templates for different analysis modes. Since the toolkit puts a special emphasis on unfamiliar domains and challenging user groups, the workshop involved the assistance of an external expert during the video analysis process. Through the UGVs the participants were able to immerse themselves into the subject and get familiar with the problem domain rapidly. The components of the toolkit such as video analysis samples and templates were also used as guidelines to interpret UGVs. The expert was directly involved in the video analysis process, and made clarifications and remarks concerning the participants’ questions and comments for gaining insights. The paper reports the outcome and findings of this half-day workshop and discusses the key insights for further developing the toolkit.