Presenting the Wikidata Human Gender Indicators

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For many years Wikipedia’s editor gender gap has been widely discussed, but its content gender gap has received less attention. This summer we presented our work in developing Wikidata Human Gender Indicators (WHGI) at OpenSym ‘16 which provides statistical insight into the composition of Wikipedia biographies through the use of Wikidata. WHGI has allowed us to research details about the character of the biography gender gap—that it is increasingly looking like the political biases of the real world—and to arm community editing groups with metrics about their work. For instance we are providing the data that allows Wikiproject Women in Red to reflect that, “[…] in November 2014, just over 15% of the English Wikipedia’s biographies were about women. Since then, we have improved the situation slightly, bringing the figure up to 16.52%, as of 9 October 2016. But that means, according to WHGI, only 232,357 of our 1,406,482 biographies are about women.”

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Investigating the Potential for Miscommunication Using Emoji

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Hey emoji users: Did you know that when you send your friend Google's grinning face with smiling eyes emoji on your Nexus, they might see Apple's grinning face with smiling eyes emoji on their iPhone? And it’s not just Google's grinning face with smiling eyes emoji; this type of thing can happen for all emoji (yes, even pile of poo emoji). In a paper (download) that will be officially published at AAAI ICWSM in May, we show that this problem can cause people to misinterpret the emotion and the meaning of emoji-based communication, in some cases quite significantly. face screaming in fear emoji, we know.

What’s more, our work also showed that even when two people look at the exact same emoji rendering (e.g., Apple's grinning face with smiling eyes emoji), they often don’t interpret it the same way, leading to even more potential for miscommunication. face screaming in fear emojiface screaming in fear emoji!

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Cross-Cultural Parenting and Technology

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Some ideas for new technology inspired by our interviews with cross-cultural families!
Some ideas for new technology inspired by our interviews with cross-cultural families!

 

My parents had two primary sources for parenting advice: my grandparents and the Dr. Spock book. If you are a parent today, you know that this is no longer the case! Millions of sources in printed literature, online, and in your local community all have opinions on how you should parent! How do parents manage so many diverse opinions? What happens when the values of the parents conflict with their community, with other family members, or even with each other? We thought that cross-cultural families (where the two parents are from different cultures or who are raising their child in a different culture from their own) may have a particularly salient perspective to offer on these important questions.

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MovieLens Datasets: Context and History

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The MovieLens datasets are full of data describing how people rate movies. As it turns out, these datasets have been useful to lots of folks, from recommender systems researchers to the readers of popular-press programming books. Though it is difficult to measure the full extent of the datasets’ impact, we see that they were downloaded more than 140,000 times in 2014, and that the keyword “movielens” currently results in over 8,900 results in Google Scholar.

It is tempting to view these collections of ratings as a cohesive whole. However, the truth of the matter is that the datasets are the product of 17 years of member activity in a web site that has seen its fair share of changes and experimental features. Given the extent of attention — research and otherwise — given to these datasets, it seems worth exploring the relationship between the system and the resulting data.

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