Как группы людей ведут себя в публичных пространствах: исследование и визуализация студии SWA Group. Каждому паттерну поведения придумано забавное название — например, «Эффект пончика»: https://www.theguardian.com/cities/gallery/2019/aug/01/lizarding-and-flex-allure-how-do-you-use-your-city-plaza-in-pictures-field-guide

Mobility as a Service

https://whimapp.com/about-us/

Обзор книжки выше

HUMAN-CENTERED ALGORITHMS AND ETHICAL PRACTICES TO UNDERSTAND DEVIANT MENTAL HEALTH BEHAVIORS IN ONLINE COMMUNITIES

Telegram опубликовал в открытом доступе тестовую версию своего блокчейна — TON. Ссылки на архивы с файлами и хранилище на Github размещены на тестовом сайте ton.org.

Подробнее:
https://tjournal.ru/115571

https://tonlabs.io/main

We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary.
We show that this extractive step significantly improves summarization results. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher rouge scores. Note: The abstract above was not written by the authors, it was generated by one of the models presented in this paper

https://arxiv.org/abs/1909.03186

"We know from Facebook’s own research that passive experiences leave people feeling worse. It’s when people interact with each other — when they actively participate in the digital forum — that they report higher levels of well being."

https://newsroom.fb.com/news/2017/12/hard-questions-is-spending-time-on-social-media-bad-for-us/

Other "hard questions" https://newsroom.fb.com/news/category/hard-questions/

Эффект Рингельмана -- эффект социальной лени

https://en.wikipedia.org/wiki/Ringelmann_effect
https://en.wikipedia.org/wiki/Social_loafing

Есть формула, которая апроксимирует средний вклад каждого участника. Работает на группе до 7 человек и только для физической активности. Есть вариант и для бОльших групп, и для knowledge workers (надо копать отдельно):
С = 100 - 7*(К-1)
где С — средний индивидуальный вклад участников; К — количество участников группы.

Можно подумать, как с этой помощью моделировать эффективность команд/соц.групп.

Книжка по теме
https://www.amazon.com/Group-Dynamics-5th-Donelson-Forsyth-ebook/dp/B01AJ8YU2Q

Дискуссия со ссылками и спорами
https://www.facebook.com/teodorix/posts/10220400256226704

Apple's CHI 2019 research paper on app usage in older adults

To characterize smartphone usage among older adults, we collected iPhone usage data from 84 healthy older adults over three months. We find that older adults use fewer apps, take longer to complete tasks, and send fewer messages. We use cognitive test results from these same older adults to then show that up to 79% of these differences can be explained by cognitive decline, and that we can predict cognitive test performance from smartphone usage with 83% ROCAUC. While older adults differ from younger adults in app usage behavior, the “cognitively young” older adults use smartphones much like their younger counterparts. Our study suggests that to better support all older adults, researchers and developers should consider the full spectrum of cognitive function.

International Conference on Web and Social Media (ICWSM)

https://www.icwsm.org/2020/index.html

In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring a large set of Reddit communities, we reveal that loyalty is manifested in remarkably consistent behaviors. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating that they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user’s propensity to become loyal is apparent from their initial interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.

User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing.

Conference: https://www.um.org/

Inferring User Interests in Microblogging Social Networks: A Survey
Guangyuan Piao, John G. Breslin

https://arxiv.org/abs/1712.07691

People + AI Research

https://design.google/library/ai/