|//No Comment - Productive, Anxious, Lonely, #HashtagWars & Fun Facts|
|Written by Alex Armstrong|
|Tuesday, 13 December 2016|
The web connects us to each other and to information in ways unimagined only a decade or so ago. It has become a research topic.
• Productive, Anxious, Lonely - 24 Hours Without Push Notifications
• #HashtagWars: Learning a Sense of Humor
• Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
Sometimes the news is reported well enough elsewhere and we have little to add other than to bring it to your attention.
No Comment is a format where we present original source information, lightly edited, so that you can decide if you want to follow it up.
Productive, Anxious, Lonely - 24 Hours Without Push Notifications
"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."
#HashtagWars: Learning a Sense of Humor
"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?
Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
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.
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."
Well I didn't know that!
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|Last Updated ( Wednesday, 14 December 2016 )|