During the past two decades, governments across the globe have been utilizing the online space to provide their information and services. Studies report that several categories of population, including older adults, report relatively low rates of obtaining governmental information and services using the Internet. However, little attempt has been made to further understand what differs between e-government adopters and non-adopters in later life. The goal of the current study was to examine socio-demographic disparities in e-government use among older adults through the lens of the digital divide approach. The data for the current study were attained from the 2017 Israel Social Survey. The sample (N = 1173) included older adults (aged 60 and older) who responded either positively or negatively on the item assessing the e-government use three months prior to the survey. Logistic regression served for the multivariable analysis. The results suggest that being male, of younger age, having an academic level of education, being married and using the Internet on a daily basis increase the likelihood of e-government use among older adults. These results lead to the conclusion that the digital divide characterizes e-government use in later life, similar to other uses of the Internet. The results emphasize the need for further socialization among older adults in using government services. This in light of the ongoing transition of these services into the online sphere, of numerous advantages of the online provision of these services, and of their major relevance in later life.

Relevance & Research Question: People tend to utilize multiple sources of health information. Although ethnic differences in online health information search has been studied, little is known about such differences in utilization of specific online health information sources and their variety. The research question is: do ethnic groups differ in their likelihood of utilizing online sources of health information?

Methods & Data: The data were attained from the 2017 Israel Social Survey. The study population included adults aged 20 and older (N = 1764). Logistic regression was used as a multivariate statistical technique.

Results: Jews were more likely than Arabs to search for health information using the call centers or sites of Health Funds and other sites, and more likely to search for health information using more than one type of site. In contrast, Arabs were more likely to search for health information on the website of the Ministry of Health.

Added Value: The study used social inequality theories for examination of ethnic differences in use of online health information sources, while referring to specific sources of such information and their variety.

Relevance and research question:

In recent years, the video platform YouTube has become a more and more important source of information, particularly for young users. During the Covid-19 pandemic, almost a fifth of all Germans used YouTube to find information on the pandemic. At the same time, disinformation on social media reached a peak in what the WHO called an “infodemic”. Therefore, we set up a study on the amount of disinformation and media diversity in YouTube’s video recommendations. As recommendations are an important driver of video reach, they were interested in whether YouTube’s recommendation algorithms promote disinformation. Moreover, the analysis set out to determine which videos, channels or topics dominate video recommendations.

Methods and data:

The study consisted of a three-step research design. As a first step, a custom-built algorithmic tool recorded almost 34.000 YouTube recommendations consisting of over 8.000 videos. After enriching those with metadata, we quantitatively analysed variables like channel type, number of views or likelihood of disinformation. In a second step, we selected 210 videos and quantitatively coded them on the specific topic or amount of disinformation. Finally, a qualitative content analysis of 25 videos delved into the characteristics and commonalities of disinformative videos. The poster will detail the specific make-up of this three-step methodology.

Results:

Our study showed that on the one hand, YouTube seems to have altered its recommendation algorithms so they recommend less disinformation, even though it is still present. However, on the other hand, the algorithm severely limits media diversity. Only a handful of videos and channels dominate the recommendations, without it being apparent which characteristics make a video more likely to be recommended.

Added value:

This study shows how a “big data” approach can be combined with more traditional research methodologies to provide extensive insight into the structures of social media. Moreover, it helped assess the extent of disinformation on YouTube and provided a window into what and how social media recommendation algorithms prioritise.

Relevance & Research Question: Online accommodation platforms are a currently unused source to investigate the demand side of the housing market in urban areas. I show how this data source can serve to study individual residential preferences (RP) from different social groups for 236 mentioned districts from 4 German cities. Those information are otherwise hardly collectable by regular survey methods. Furthermore, I build a comparable “socio-economic residential preferences index” (SERPI) for each district and city based on the different residential preferences from academics and jobseekers.

Methods & Data: I scraped between Juli 2019 and April 2020 housing requests uploaded on Ebay-Kleinanzeigen from 8 German cities. I collected 19.123 individual requests. Online accommodation requests serve as good data source for natural language processing tasks as they are highly structured. I used named entity recognition and word matching to extract i) (informal) (sub-)district residential preferences and ii) socio-economic characteristics of the apartment seekers. Those were for instance employment status, occupational status, family status or maximum rents. I assume a biased sample towards those individuals who ‘seize every chance’ to find a new apartment.

Results: I find the highest differences between residential preferences from academics and jobseekers in Hamburg (SERPI=0.89), Munich (0.83), Cologne (0.73) and least differences in Berlin (0.36). Among 236 districts in those four cities, the district "Farmsen-Berne" (Hamburg) shows the strongest concentration of residential preferences from jobseekers, but almost no RP from academics (SERPI = -5.32). The strongest concentration of RP from academics can also be found in Hamburg in "Sternschanze" (3.10). Berlin's districts show the lowest levels of RP segregation. (More on the dashboard https://germanlivingpreferences.herokuapp.com/ (user: kleinanzeigen, pw: showme))

Added Value: The study presents a new approach for urban research to investigate residential preferences from actual apartment seekers - potentially in real time. The SERPI is a new instrument to investigate spatial segregation and gentrification processes. Further research could - for instance - investigate the causes of group specific RP.