On November 9th, the Federal Emergency Management Agency(FEMA)conducted its first national test of the Emergency Alert System. In some communities this meant full involvement, with teams responding to mock emergencies, and managers monitoring the execution. In the deaf community, the response to monitor was regarding two Twitter hashtags, #SMEM, and #DEMX. The #SMEM hashtag is specific to the emergency response community, and was created over a year ago, and the #DEMX hastag is specific to the deaf community, but created specifically for this event. Monitoring the usage of these hashtags was Steph Jo Kent, a PhD. Candidate in Communications at the University of Massachusetts. Steph’s goal was to monitor the spread of these hashtags throughout the deaf community and emergency response community and how they crossed channels. In order to do this, she utilized DiscoverText, which is how I was lucky enough to become involved in the project. Monitoring these specific Tweets adds to the already diverse functionality of DiscoverText. To start the project, we simply used the Twitter API to harvest uses of #SMEM and #DEMX beginning on November 2. After the event on November 9, we continued to harvest uses of the hashtags. By early December, we had archived nearly 800 Tweets using the hashtag #DEMX, and nearly 8,000 Tweets using the hashtag #SMEM. From these two archives, it is possible to breakdown Tweets by time and person, giving us valuable information about key individuals and how they spread the hashtag. For Steph’s research, it was particularly valuable to isolate the crossover between the two hashtags. Using our search feature, we were able to isolate cases of crossover and bucket those results. This allows us to move from noisy data, to a more manageable and germane grouping of Tweets. From here, we utilized the newly optimized TopMetafeature to breakdown the occurrences by day and by user. We were able to discover which days and individuals produced the most Tweets. The information we found allowed us to better visualize how the Tweets broke down before and after the event. The results showed a small number of users producing the majority of Tweets, and that prior to the event, there was more usgage of the hashtags. Unfortunately, the mass crossover of Tweets that we had envisioned did not occur. There was a minimal amount of crossover, meaning the message did not travel well through the two communities. Steph has posted a detailed analysis of her findings on her blog, where she uses her expertise to analyze the project. In the future, this same methodology can be applied to hashtags that have been created for marketing or other purposes, such as hashtags for television shows and large events. There is valuable information in these hashtags; they reflect an emergent folksonomy that influences how ideas, links and memes spread over Twitter. Using the GNIP Power Track, these hashtags can be leveraged as metadata, broken down over time and used to display how well information did or did not travel. Overall, this was great experiment, and I am happy to have had the opportunity to collaborate with Steph, and to have participated in a project that has the power to influence the way social media is used to interact those in the deaf community.
The evolution of the API opens the door for third-party developers to access information on social media networks. In the best case, this provides a healthy, democratic flow of information. Yesterday, DiscoverText had “rate limits” imposed in terms of its access to Twitter data. As written, the Twitter API allows unauthenticated calls of 150 per hour, per IP address. Authorized calls (users logged on using their Twitter credentials, also known as OAuth) allow for up to 350 calls per hour, per person. In addition, the Twitter Search API has internal rate limiting mechanisms, but Twitter does not publish those specific limitations for fear of abuse. Going over any of these limits results in the user being presented with “Error 420”, which simply means that the user is being rate limited. This hampers the ability to harvest twitter feeds within DiscoverText. We have never had rate limit problems prior to this, but according to timestamps on articles posted on Twitter’s developer website, Twitter might have become more cognizant of those harvesting large amounts of data (not just us), and as a result, are cracking down on heavy users. At Texifter, we fully respect the rules and regulations of the Twitter API, and in no way seek to disobey or bend these set rules in our flagship software product, DiscoverText. On August 18, 2011, the same day we learned of the 420 errors, we performed emergency maintenance to better cope with Twitter rate limitations. We also wanted to more gracefully handle rate limitation errors and to ensure we abide by Twitter Terms of Service. With that said, in order to continue our ability to harvest information from Twitter and perform our cutting-edge research, we are currently exploring easier and more reliable ways to harvest data. The maintenance performed on DiscoverText stills allow 1500 items per fetch as determined by Twitter’s architecture on the public API. In addition, no extraneous error messages should result when DiscoverText is being rate limited. Some searches might be silently delayed for 5 minutes, however, these fetches will catch up as soon as they can. In the near future, look for new developments for DiscoverText. We’ve got big plans for our social media API fetching that will greatly enhance our user’s ability to receive timely and actionable social media feeds. We don’t want to reveal too much right this moment, but we’re sure you’ll like what we have in store and in traditional Texifter style, we’ll plan a large announcement when the time is right.