Two.42

The name has people scratching their heads, but Two.42.solutions is fast-becoming a leader in media monitoring, with a little bit of help from DataSift. Let’s deal with that company name first. Mohammad Hamid, chief technology officer of two.42.solutions, explains its origins. “When we formed the company we performed a number of experiments to identify the appropriate monitoring methodology. Some 67% of the experiments had outliers—and the value of those outliers 67% of the time was…

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i am a caption

In the second of a two-part blog series, we look in depth at how Tumblr data captured by Nexalogy and DataSift social media platforms can inform drug makers as they seek to understand and support users. In the last blog, we discovered how the blend of the Nexalogy social data analysis platform and the DataSift social data platform is being used to yield Tumblr insight and reveal patient perspectives of mental health medication. It’s time…

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Mental health 1

In a two-part blog series, we explore how data mined from Tumblr using Nexalogy and DataSift can provide valuable insight into an audience that has traditionally been very difficult to reach. Mental health. It’s tricky for doctors to treat, for patients to live with and for the makers of medication to address. The nature of mental illness also makes it difficult to gain insight into patients’ experiences on a human rather than a clinical level….

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i am a caption

Are you a data packrat? Does your business keep and receive an enormous amount of data? If so, do you just stuff it somewhere and hope to figure out what to do with it some day? 70% of the battle with Big Data is making it manageable so that it can then be meaningful to you. Business insight and valuable intelligence remain buried on disks and tapes unless you have a way to making access…

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i am a caption

I was recently discussing a Big Data initiative with a British financial services organization. They had made significant investments in their infrastructure and now wanted ‘to fill up’ their Big Data lake. The DataSift platform is a Big Data platform that processes over 2.1bn unique items of unstructured human-generated data every day. The interesting way the client phrased their objective made me think about ways to measure volume when working with social and news data….

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how_to_2

Your marketing campaign is about to launch and you need to track the response. Not just on Twitter or Facebook but across all the networks, all languages, and the long-form content such as blogs and discussion boards. You know that DataSift aggregates all these sources so you can write one simple filter. You know we have one simple API to hit for the filtered interactions, but where’s your developer gone? The last time you did…

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If you’ve read my previous blogs, you’ll know that our platform allows you to filter vast amounts of real-time data. You end up with all the interactions you’re interested in – but how do you get those interactions from our platform to your app or analytics tool? There are three ways: 1. Stream them from our platform in real time 2. Have our platform send them to a destination of your choice 3. Pull them…

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Big Data

Humans are social creatures. That might even be truer when we go online. Consider the following recent (beginning portion of 2014) statistics about the most popular social network and blog platforms and their memberships: Twitter has 560 million registered users, and over half of them are active, and at least as of a couple of years ago, there were 200 million tweets sent every single day. WordPress powers 20% of the top global sites and…

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ms-files (1)

In our latest of use case posts, we will look at two brands – Home Depot and Lowes – and compare their share of voice. Does one brand have more social presence than the other? Does one brand outperform the other in terms of volume or hashtag and mention usage? How do they compare geographically? Here’s a simple, end-to-end scenario for collecting, consuming and analyzing DataSift data to discover your share of voice. Let’s find…

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ms-files (2)

In an article titled “Watch how word of Ebola exploded in America” Time.com last week published data showing how 10.5 million tweets referenced Ebola between September 16 and October 6. Predictably there has been a massive surge in the conversation since the first Ebola diagnosis inside the USA on September 30. The article features a graph from @twitterReverb which captures this surge brilliantly, but also highlights some of the limitations of relying solely on traditional…

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