The sort of social connectivity that we have is a very short lived phenomenon. It could be changing the nature of society or the nature of human existence. Only a few years ago the sort of isolation that this new research looks at was the norm not the exception:
"We report from the Do Not Disturb Challenge, where 30 volunteers disabled notification alerts for 24 hours across all devices. We isolated the effect of the absence of notifications on the participants through an experimental study design: we compared self-reported feedback from the day without notifications against a baseline day.
The evidence indicates that notifications have locked us in a dilemma: without notifications, participants felt less distracted and more productive. But, they also felt no longer able to be as responsive as expected, which made some participants anxious. And, they felt less connected with one's social group.
Moreover, we found evidence that people may start to feel overloaded by notifications: in contrast to previous reports, more about half of the participants began to disable or manage notifications more consciously after the study."
Hashtag? What's a hastag? So new and yet so much part of the social scene that it has spread well beyond its orignal invention. Today people think in terms of hashtags as a categorization of messages and information in general. Hashtags and humour also seem to go together:
"In this work, we present a new dataset for computational humor, specifically comparative humor ranking, which attempts to eschew the ubiquitous binary approach to humor detection. The dataset consists of tweets that are humorous responses to a given hashtag.
We describe the motivation for this new dataset, as well as the collection process, which includes a description of our semi-automated system for data collection. We also present initial experiments for this dataset using both unsupervised and supervised approaches.
Our best supervised system achieved 63.7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks. Initial experiments indicate that a character-level model is more suitable for this task than a token-level model, likely due to a large amount of puns that can be captured by a character-level model."
And some examples?
As I Lay Dying of congestive heart failure @midnight #FastFoodBooks Harry Potter and the Order of the Big Mac #FastFoodBooks @midnight The Girl With The Jared Tattoo #FastFoodBooks @midnight A Room With a Drive-thru @midnight #FastFoodBooks
Wikipedia - what's that? An encyclopedia written by the crowd? Never! You need experts for that. Wikipedia is probably the most unexpected sucess story of social media. After several false starts a format was found and the encyclopedia was written. But we all know that there is a lot of trivia in there as well as important facts - so why not try picking the triva out and mining some fun facts:
"A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity.
Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia.
In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality.
Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention."