Loneliness and Interactive Online Experiences
Bibliographic record
Abstract
In the United States, there are growing concerns about the prevalence and severity of loneliness. In a study conducted in 2008, twenty percent of individuals reported feeling sufficiently isolated for it to be a major source of unhappiness in their lives (Cacioppo, 2008). In a 2018 study of 20,000 adults, nearly half reported sometimes or always feeling that no one knows them very well (Cigna, 2018). Other Western countries report similar trends. Research in the United Kingdom suggests that roughly 200,000 British citizens haven’t had a conversation with a friend or relative in the past month (ageuk, 2014). Unfortunately, the COVID-19 pandemic forced an already lonely population to isolate further. In the early phases of the outbreak, and during subsequent surges of the virus, many countries imposed lockdowns and strict social distancing protocols. Yet, despite fears that loneliness rates would skyrocket during this time, several studies suggest that mean-levels of loneliness did not change significantly for either the United States (Luchetti et al., 2020) or the United Kingdom (ons.gov.uk, 2020). Though certain groups (such as college students) have been affected disproportionately (Elmer et al., 2020, Groarke et al., 2020), the rates for the general population have not been as troubling as many predicted. Research on loneliness during COVID-19 is still ongoing and more work is needed to better understand these trends. It is possible that new behaviors during the pandemic helped counteract the wave of loneliness many were expecting. New technological innovations and cultural practices, particularly with regards to video chatting, may have maintained some degree of social connectedness, in spite of social distancing measures. It is also important to note that loneliness is often described as a subjective emotional state, characterized as the perception of social isolation (Holt-Lunstad et al. 2015). Individuals can be physically distanced, without being socially distanced and this distinction likely has health implications, especially during the pandemic (Aminnejad & Alikhani, 2020). During the pandemic, many companies embraced the affordances of video chat and developed new products to help people connect over video. Airbnb, for example, created a video chat product that allows would-be travelers to engage in cultural experiences across the world using Zoom (a popular video chat and video conferencing platform). The product (“Online Experiences”) was developed to simulate elements of local tourism through interactive virtual walking tours (led and designed by local tour guides), cooking shows, and science lessons from around the world (“Enjoy the Magic of Airbnb Experiences”, 2021). It builds on established online formats, such as Webinars (Gegenfertner & Ebner, 2019) or live streams such as Twitch or YouTube Live (Pires & Simon, 2015), but it is designed to promote deeper levels of social interactivity and connection. The sessions impose a limited group size and emphasize video and audio interactions between audience members and the presenters. In their marketing materials, Airbnb presents these experiences as “a great way to connect with people around the world.” However, it is unclear whether this format might offer deeper feelings of social connectedness than more passive viewing experiences. The main aim for this controlled experiment is to assess how two types of online experiences might differentially affect feelings of loneliness, social connectedness, and affect (positive and negative). We will compare two different online formats: 1) a socially interactive experience designed to mimic Airbnb’s Online Experiences and 2) a more passive viewing experience in the style of a webinar. Insights from this experiment could help us understand how new online experiences might affect feelings of social connectedness and isolation. REFERENCES Aminnejad, R., & Alikhani, R. (2020). Physical distancing or social distancing: that is the question. Canadian Journal of Anesthesia/Journal canadien d'anesthésie, 67(10), 1457-1458. Cigna US loneliness index. https://www.cigna.com/static/www-cigna-com/docs/about-us/newsroom/studies-and-reports/combatting-loneliness/loneliness-survey-2018-updated-fact-sheet.pdf. Accessed on 5 April 2021. Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. Plos one, 15(7), e0236337. Enjoy the magic of Airbnb experiences (April 10, 2020). Retrieved from https://www.airbnb.com/s/experiences/online. Ettman, C. K., Abdalla, S. M., Cohen, G. H., Sampson, L., Vivier, P. M., & Galea, S. (2020). Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA network open, 3(9), e2019686-e2019686. Gegenfurtner, A., & Ebner, C. (2019). Webinars in higher education and professional training: a meta-analysis and systematic review of randomized controlled trials. Educational Research Review, 28, 100293. Groarke, J. M., Berry, E., Graham-Wisener, L., McKenna-Plumley, P. E., McGlinchey, E., & Armour, C. (2020). Loneliness in the UK during the COVID-19 pandemic: Cross-sectional results from the COVID-19 Psychological Wellbeing Study. PloS one, 15(9), e0239698. Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspectives on psychological science, 10(2), 227-237. Luchetti, M., Lee, J. H., Aschwanden, D., Sesker, A., Strickhouser, J. E., Terracciano, A., & Sutin, A. R. (2020). The trajectory of loneliness in response to COVID-19. American Psychologist. Pires, K., & Simon, G. (2015, March). YouTube live and Twitch: a tour of user-generated live streaming systems. In Proceedings of the 6th ACM multimedia systems conference (pp. 225-230).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".