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Tools for Text: Friday Winners

December 18, 2015 by Stuart Shulman Leave a Comment

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We did it again. Nine Fridays in a row we have drawn two winners for our Tools and Data Give-A-Way. Today’s lucky winners are @yelenamejova & @jaganadhg for these Tweets. We are doing one final give-away next friday. Who will get prize #19 & 20?

Check out https://t.co/2GELWhAnE6 for highly customizable comprehensive historical Twitter datasets.

— Yelena Mejova (@yelenamejova) October 20, 2015

Submitted query for estimate at #Snifer https://t.co/bdOmRsk1gs. it is really coll solution for sm data.

— Jaganadh G (@jaganadhg) November 3, 2015

Filed Under: DiscoverText, general, GNIP, Social Media, Twitter

It Must Be Friday

December 4, 2015 by Stuart Shulman Leave a Comment

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We are giving away software and custom historical Twitter prizes today, so it must be Friday. Congratulations to @karawhytas and @Bpowder87 who won based on these fine Tweet reviews of Sifter. There are still three drawings left before we close out the year. There is no time like the present to try out Sifter and write your review. 

Just created a historical Twitter search on https://t.co/ezK05ZM3lk! Great tool for Finance research! Try it out! @discovertext @texifter

— Maximilian Franke (@Bpowder87)

Sifter is the most efficient way to gather historical Twitter data. https://t.co/u9PdYN3odg

— Kara Whytas (@karawhytas)

Filed Under: general, GNIP, Social Media, Texifter, Twitter

New Historical Twitter Prizes

October 17, 2015 by Stuart Shulman Leave a Comment

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Win Historical Twitter Data with Easy Steps

Last year, we held a series of drawings that gave winners Historical Twitter days, Tweet data, and Enterprise account access to DiscoverText. Some of the winners have gone on to do amazing and influential work on the methods and tools used to research Twitter. As a result, we now list 70 scholarly mentions of DiscoverText.

So, brace yourself folks, because in honor of #TwitterFlight, we are doing it again this year and instead of once a month we will draw two winners on Fridays every week for the remainder of 2015. The prize for each winner is ten free Historical Twitter days, 100,000 Tweet credits, and three-months of Enterprise access to DiscoverText.

​There will be a weekly drawing of two winners at random. The prizes will be drawn every Friday morning, starting on October 23. The contest will be held every week for the rest of 2015. You have many chances of winning, with a limit of one prize per unique Twitter handle during 2015. Are you ready to win some prizes? Here is a step by step guide on how to enter the contest.

  • Step #1. Test Sifter. You can test Sifter by submitting a valid estimate. Sifter is a free estimate service provided by Texifter that allows you to test query rules (as well as the ability to filter and retrieve data for a fee) from the complete, undeleted history of Twitter. Powered by Gnip.com, it estimates the number of Tweets responsive to the query and the cost of the retrieval.  Read what past customers have said about Sifter and register for a free account.
  • Step #2. Submit your entry as a Tweet. When you submit your entry, include the link sifter.texifter.com, as well as a <140-character review of the product. Do not forget the URL. Only one entry per Twitter ID is allowed per drawing, but those of you with more than one Twitter handle can submit multiple entries.
  • Step #3. Wait for the announcement of winners. The winners will be drawn every week starting on October 23. Check the Texifter blog to find out if you are one of the lucky winners. Of course we will Tweet about it too.

Are you excited? We definitely are! By testing Sifter’s powerful capabilities, you get a chance to win 10 Historical Tweet days, 100,000 Tweets and 3-month Enterprise access to DiscoverText, which includes one Enterprise account and two Professional accounts. Find out if Sifter and DiscoverText are right for you and win these amazing prizes without spending a penny. We want your social currency.

  • Ten Historical Twitter Days & 100,000 Tweets: Winners need to collect their prize within seven days of the announcement.
  • Two Professional Accounts: The Professional account, which regularly costs $99/month, includes 100,000 units of social or text data from any source, 25 live data feeds, one professional seat and unlimited projects. It features analytic and reporting tools, adjudication tools, CloudExplorer, and standard support.
  • One Enterprise Account: The Enterprise account, which costs $3,000 a month, includes 1,000,000 units of social or text data from any source, 100 live data feeds, two professional seats, one enterprise seat, and unlimited projects. It includes all Professional features plus ActiveLearning, FOIA toolkit access, and priority support.

For more information about Texifter’s social data offer and our advanced analytical tools, please email us at info@texifter.com. 

 

 

Filed Under: DiscoverText, general, GNIP, Twitter

Gnip Geo Enhancements for Twitter

February 7, 2015 by Stuart Shulman 1 Comment

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In a continuing effort to create the best possible methods to sample Twitter data, we testing out a number of Gnip geographical enhancements. For a limited time, all of the “profile” PowerTrack rules are live. You can add them to your free estimate queries in Sifter or use them in your day-forward PowerTrack rules on DiscoverText to greatly increase the amount and granularity of the geographical specificity in the results.

The Geo and Profile Enhancement Rules
has:profile_geo
Matches tweets that have any Profile Geo metadata, regardless of the actual value. Here are two sample estimates, one for a day with no keyword and another for a month with the keyword Nike:

Rule Text: has:profile_geo
Start Date: 01/01/2015
End Date: 01/01/2015
Estimated Activities: 92,450,000

Rule Text: Nike has:profile_geo
Start Date: 01/01/2015
End Date: 01/31/2015
Estimated Activities: 1,300,000

has:profile_geo_locality
Matches all activities that have a profileLocations.address.locality value present in the payload. Here is a one month sample estimate:

Rule Text: profile_country_code:us has:profile_geo_locality
Start Date: 01/01/2015
End Date: 01/31/2015
Estimated Activities: 413,851,000

has:profile_geo_subregion
Matches all activities that have a profileLocations.address.subRegion value present in the payload. Here is a sample estimate for one month of every Tweet with a geo subregion.

Rule Text: has:profile_geo_subregion
Start Date: 01/01/2015
End Date: 01/31/2015
Estimated Activities: 400,634,000 

has:profile_geo_region
Matches all activities that have a profileLocations.address.region value present in the payload. Here is a 10% sample estimate for 1 day:

Rule Text: sample:10 has:profile_geo_region
Start Date: 01/01/2015
End Date: 01/01/2015
Estimated Activities: 6,864,000

profile_bounding_box:[west_long south_lat east_long north_lat]
Uses  latitude and longitude to create a geographical bounding box. Here is an example of a one month estimate for the bounding box for Boulder, CO. 

Rule Text: profile_bounding_box:[-105.301758 39.964069 -105.178505 40.09455]
Start Date: 01/01/2015
End Date: 01/31/2015
Estimated Activities: 412,000

profile_country_code:
Exact match on the “countryCode” field from the “address” object in the Profile Geo enrichment. Uses a normalized set of two-letter country codes, based on ISO-3166-1-alpha-2 specification. This operator is provided in lieu of an operator for “country” field from the “address” object to be concise. Here is an example of one week of Twitter with the country code Brazil:

Rule Text: profile_country_code:BR
Start Date: 01/01/2015
End Date: 01/07/2015
Estimated Activities: 42,694,000

profile_region:
Matches on the “region” field from the “address” object in the Profile Geo enrichment. This is an exact full string match. It is not necessary to escape characters with a backslash. For example, if matching something with a slash, use “one/two”, not “one\/two”. Use double quotes to match substrings that contain whitespace or punctuation.

profile_region_contains:
Matches on the “region” field from the “address” object in the Profile Geo enrichment. This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation. Here is an example of one week for region contains Seattle or New England.

Rule Text: profile_region_contains:seattle OR profile_region_contains:new england
Start Date: 01/01/2015
End Date: 01/07/2015
Estimated Activities: 7,000

profile_locality:
Matches on the “locality” field from the “address” object in the Profile Geo enrichment. This is an exact full string match. It is not necessary to escape characters with a backslash. For example, if matching something with a slash, use “one/two”, not “one\/two”. Use double quotes to match substrings that contain whitespace or punctuation. Here is an example of 1 month for avon:

Rule Text: profile_locality:avon
Start Date: 01/01/2015
End Date: 01/31/2015
Estimated Activities: 74,000

profile_locality_contains:
Matches on the “locality” field from the “address” object in the Profile Geo enrichment. This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation. Here is an example of one week for york:

Rule Text: profile_locality_contains:york
Start Date: 01/01/2015
End Date: 01/07/2015
Estimated Activities: 3,660,000

profile_subregion:
Matches on the “subRegion” field from the “address” object in the Profile Geo enrichment. In addition to targeting specific counties, these operators can be helpful to filter on a metro area without defining filters for every city and town within the region. This is an exact full string match. It is not necessary to escape characters with a backslash. For example, if matching something with a slash, use “one/two”, not “one\/two”. Use double quotes to match substrings that contain whitespace or punctuation. Here is an example of one week for San Francisco County:

Rule Text: profile_subregion:”San Francisco County”
Start Date: 01/01/2015
End Date: 01/07/2015
Estimated Activities: 1,170,000

profile_subregion_contains:
Matches on the “subRegion” field from the “address” object in the Profile Geo enrichment. In addition to targeting specific counties, these operators can be helpful to filter on a metro area without defining filters for every city and town within the region. This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation.

                                      

Filed Under: DiscoverText, GNIP, Social Media

Twitter’s Complete Index is Live

November 20, 2014 by Stuart Shulman Leave a Comment

Tweet

Twitter recently announced that the indexed data at https://twitter.com/search-advanced would stretch all the way back to the dawn of Twitter’s timeline “between Dec. 30, 2006 and Jan. 2, 2007.” This is an impressive feat representing a massive capability upgrade. This new service lets users glimpse into any day or days in Twitter history to display the Tweets responsive to simple keyword or exact phrase queries using basic language filters. There are also intuitive filters for filtering by user accounts and places. The result is a seemingly limitless cascade of Tweets in the familiar Twitter display mode. We think this is a powerful new tool that should be used in conjunction with our Sifter historical Twitter data tool to test out keywords and phrases that may be used when creating free estimates to license Twitter data. For more information, watch our video on using Gnip PowerTrack filters then contact us about how to us Twitter’s complete index to improve the quality and performance of your historical Twitter research.    

Filed Under: GNIP, Social Media, Texifter, Twitter Tagged With: filter, filters, search, Tweets, twitter, Twitter Analysis, Twitter API, Twitter Mining

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Oh, Canada? A quick research note: your election is definitely under attack via Twitter, despite assertions to the contrary. This is part one of a nine-part series written in the run-up to the 2019 Canadian election. The research was reported by CTV and CBC. Twitter Data Collection on Canadian Elections For the last month, we […]

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