Twitter has acquired Gnip, a social data provider that we have covered in the past. According to Chris Moody of Gnip, “Combining forces with Twitter allows us to go much faster and much deeper. We’ll be able to support a broader set of use cases across a diverse set of users including brands, universities, agencies, and developers big and small. Joining Twitter also provides us access to resources and infrastructure to scale to the next level and offer new products and solutions. This acquisition signals clear recognition that investments in social data are healthier than ever. Our customers can continue to build and innovate on one of the world’s largest and most trusted providers of social data and the foundation for innovation is now even stronger. We will continue to serve you with the best data products available and will be introducing new offerings with Twitter to better meet your needs and help you continue to deliver truly innovative solutions.” Read more
Posts Tagged ‘Twitter’
This week saw schema.org introduce vocabulary that enables websites to describe the actions they enable and how these actions can be invoked, in the hope that these additions will help unleash new categories of applications, according to a new post by Dan Brickley.
This represents an expansion of the vocabulary’s focus point from describing entities to taking action on these entities. The work has been in progress, Brickley explains here, for the last couple of years, building on the http://schema.org/Action types added last August by providing a way of describing the capability to perform actions in the future.
The three action status type now includes PotentialActionStatus for a description of an action that is supported, ActiveActionStatus for an in-progress action, and CompletedActionStatus, for an action that has already taken place.
Jessica McKenzie of Tech President reports, “An international group of researchers led by the University of Sheffield is building a social media “lie detector” named Pheme, after the mythological rumormonger, that can determine in real time whether a information spread on social media is true or false. The idea is that identifying misinformation would allow journalists, government agencies, emergency response, health providers and private companies to respond to emergencies and other events more effectively.” Read more
Twitter is looking for a Software Engineer – NLP Arabic in San Francisco, CA. According to the post, “Twitter is looking for engineers who are passionate about delivering a great user experience to our Arabic speaking users. Based in Twitter’s San Francisco HQ, you will work closely with our product management team, interaction designers and localization specialists on bringing new infrastructure or product features to our Arabic speaking markets. Potential projects includes overhauling our Arabic language tokenization, Right-to-Left language support for Android, iOS and Web clients, and precision improvements on Arabic Trending topics. You will also work on other Right-to-left languages including Urdu, Farsi, and Hebrew.” Read more
Last week, Raffi Krikorian of Twitter announced that Twitter is “introducing a pilot project we’re calling Twitter Data Grants, through which we’ll give a handful of research institutions access to our public and historical data. With more than 500 million Tweets a day, Twitter has an expansive set of data from which we can glean insights and learn about a variety of topics, from health-related information such as when and where the flu may hit to global events like ringing in the new year. To date, it has been challenging for researchers outside the company who are tackling big questions to collaborate with us to access our public, historical data. Our Data Grants program aims to change that by connecting research institutions and academics with the data they need.” Read more
Elizabeth Harrington of Free Beacon reports, “The federal government is studying how to use Twitter for surveillance on depressed people. The University of California, San Diego (UCSD) began a study financed by the National Institutes of Health last month that will provide ‘population level depression monitoring’ through the social media site. The project, ‘Utilizing Social Media as a Resource for Mental Health Surveillance,’ is costing taxpayers $82,800.” Read more
The enterprise version of Bottlenose has formally launched. Now dubbed Nerve Center, the service to provide real-time trend intelligence for brands and businesses, which The Semantic Web Blog previewed here, includes a dashboard featuring live visualization of all trending topics, hashtags and people, top positive and negative influences and sentiment trends, trending images, videos, links and popular messages, the ability to view trending messages by types (complaints vs. endorsements, for example) and real-time KPIs. As with its original service, Nerve Center leverages the company’s Sonar technology to automatically detect new topics and trends that matter to the enterprise.
“Broadly speaking, every large enterprise has to be doing social listening and social analytics,” CEO Nova Spivack told The Semantic Web Blog in an earlier interview, “including in realtime, which is one thing we specialize in. I don’t think any other product out there shows change as it happens as we do.” It’s important, he said, to understand that Bottlenose focuses on the discovery of trends, not just finding what users explicitly search for or track. Part of the release, he added, “will be some pretty powerful alerting to tell you when there is something to look at.”
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
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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