Posts Tagged ‘social media semantics’
How can the semantic web help you participate in and celebrate Halloween this year? We trolled around and came up with a few ideas:
* Still haven’t found just the right costume yet for tonight’s festivities (for you, that is – we’re sure the kids have had theirs planned for some time). Perhaps you’re thinking hard about a do-it-yourself skeleton theme, but aren’t sure of the details for creating the most realistic effect? Well, if you head over to semantic search engine DuckDuckGo’s science goodies section, you’ll get a quick response on the number of bones in the human body, courtesy of Wolfram/Alpha computations. You can take it from there.
* OK, costume’s in check. Now how about what to do while wearing it?
Semantic search engine SenseBot might be a help here, pointing you to information it’s extracted from web pages and summarizing them in a, well, sensible way, as well as offering a cloud of the concepts it’s discovered for you to further narrow your agenda. The results can be a little off here and there, but it’s nice to have an option to further narrow a search, like one for adult activities to partake in on Halloween, to something more granular, like those designed for the “scare” factor.
Sarah Perez of TechCrunch reports, “Fresh on the heels of its deal with Tumblr for access to the Tumblr ‘firehose,’ social data platform DataSift is putting that data, and more, to good use with the launch of a new API that performs historical analysis across Twitter, Tumblr, and Bit.ly ‘firehoses,’ as well as data pulled from forums, blogs and public Facebook data. With ‘Historic Preview,’ as DataSift is calling it, developers have the ability to perform historical analysis across all sources combined, using four different analysis types. These include: frequency distribution, numeric statistical analysis, target volume, and word count (a list of up to 100 of the most often used words). The analysis functions can be applied to all 400+ metadata fields, the company says.” Read more
Tom Simonite of the MIT Technology Review reports, “Facebook is set to get an even better understanding of the 700 million people who use the social network to share details of their personal lives each day. A new research group within the company is working on an emerging and powerful approach to artificial intelligence known as deep learning, which uses simulated networks of brain cells to process data. Applying this method to data shared on Facebook could allow for novel features and perhaps boost the company’s ad targeting. Deep learning has shown potential as the basis for software that could work out the emotions or events described in text even if they aren’t explicitly referenced, recognize objects in photos, and make sophisticated predictions about people’s likely future behavior.” Read more
In a new update, PeerIndex users can now view their full influence profiles and download all the data they’ve made available to PeerIndex with one click. The company blog states, “As a data provider we feel that the most responsible way to handle our business is to be transparent with social media users. We strongly believe in the power of data to make your interactions with brands more helpful and relevant, but at the same time, we are firmly of the opinion that you, the customer, has to have the final say in whether brands get to see your data or not. We fully comply with international data protection guidelines and best practices – such as not tracking a user when their browser header says ‘DO NOT TRACK’ – and have always ensured that our users’ choices were respected in the way we collect, analyse and publish PeerIndex data.” Read more
Leonor Sierra of The University of Rochester reports, “A new system could tell you how likely it is for you to become ill if you visit a particular restaurant by ‘listening’ to the tweets from other restaurant patrons. The University of Rochester researchers say their system, nEmesis, can help people make more informed decisions, and it also has the potential to complement traditional public health methods for monitoring food safety, such as restaurant inspections. For example, it could enable what they call ‘adaptive inspections,’ inspections guided in part by the real-time information that nEmesis provides.” Read more
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