Due to popular demand, the product development team at Texifter is proud to announce that “TopMeta” is now exportable! What does this mean, you might ask? What is TopMeta? When you import either your own data or live social media feeds into DiscoverText, that data often includes various “metadata,” providing a wealth of revealing information about the Tweet, Facebook post, public comment, or survey response you will be analyzing. “TopMeta Explorer” is the function in DiscoverText that allows you to view the number of most (or least) frequently occurring metadata items and filter your data according to that metadata. Considering the wealth of metadata that may be within your data, the ability to easily organize and filter such metadata may turn out to be the difference between substantive and inadequate research. Metadata is Power When might the organization of metadata come in handy, you may also ask? It’s easy to imagine the answer to this question when you consider the kinds of metadata you may collect from live feeds such as the public Twitter API or the GNIP PowerTrack. From those feeds alone, you may collect any of the following metadata (depending on your search method): 1) The time & date of a Tweet, 2) the account name of the tweet’s sender, 3) the real name of the tweet’s sender, 4) the “hashtags” in a tweet, 5) the account name(s) “mentioned” in a tweet, 6) the shortened URL in a tweet, 7) the expanded URL in a tweet, 8) a link to the tweet itself, 9) a direct link to the media in a tweet, 10) the geo-coordinates from which a tweet is sent, 11) the number of “followers” of a tweet’s sender, 12) the number of those “following” a tweet’s sender, 13) the date that a tweet sender’s account was created, 14) the city of the tweet sender, and 15) the “Klout” score of the tweet’s sender. Exporting TopMeta Until now, the “TopMeta Explorer” function has allowed users to easily sort this kind of metadata within DiscoverText. As of this week, this metadata can now be exported as a .CSV file, empowering Enterprise DiscoverText users to more seamlessly utilize the capabilities of DiscoverText, in tandem with their other research tools. We’ll continue to keep you posted about exciting new developments in DiscoverText as they are launched. If you are interested in trying DiscoverText for yourself, sign-up at discovertext.com and email me at email@example.com. I’ll be happy to get you started.
The use of social media has grown exponentially over the last several years. In fact, most television programs and televised advertising have a social media component, designed to expand reach and engagement with the audience. To date, the tobacco control community has relied on traditional media—paid television, radio, billboard and print media advertising—to promote their messages. On March 19, 2012, the Centers for Disease Control and Prevention (CDC) launched Tips from Former Smokers. This campaign was the CDC’s largest anti-smoking campaign ever and its first national advertising effort. The campaign will last four months and consist of both traditional and social media. The Health Media Collaboratory at the University of Illinois at Chicago, directed by Sherry Emery, PhD, will measure and evaluate a key social media component of the campaign—its Twitter reach and impact. Using DiscoverText with GNIP’s PowerTrack provides full access to Twitter’s Firehose. This is in contrast to Twitter’s publicly available API stream, which provides only a 1% sample of tweets. Because the volume of tweets for health social media campaigns are relatively low, every tweet matters. Access to GNIP’s premium Twitter feed allows us to capture all tweets and metadata for the campaign. The use of DiscoverText to sift through tweets and code for content provides a useful tool for measuring online public engagement, audience sentiment, and campaign discourse. The Collaboratory will report on the overall reach and audience engagement of the campaign through an analysis of unique users reached, number of retweets, and mentions. This information will not only track the engagement of individual users but also measure the engagement of state tobacco control programs in the campaign. A sentiment analysis will be conducted on tweets to gauge the emotional valence of the campaign and individual television ads. Finally, using root keywords for quitting and smoking uptake, the numbers of Twitter users that express interest in quitting or prevention will be reported. For more information about this project, visit the UIC Health Media Collaboratory website or follow @GLENszczypka for updates. Research funded by the National Cancer Institute (Grant No. 1U01CA154254).
This is Version 1 of the 60-second Texifter elevator pitch . Feedback and questions are truly welcome. Just email firstname.lastname@example.org . ~Thanks!
Check out this video introducing our latest experiments creating custom social “sifters” to winnow down a lake of social media data and leave behind only those items that are truly responsive to your search. Great tool, or greatest tool ever? You be the judge.