One of the oft-discussed challenges of working with social media data, especially Twitter, is the ephemeral nature of the data. If we do not collect the data in real-time, it disappears. While it is possible could purchase historical data from GNIP or DataSift; the cost is out of reach of most researchers. It’s easy to think that we’ve dodged that bullet of ephemerality once we’ve built an archive of tweets and other social media data about a specific event or topic. But have we?
What about the URLs in these tweets?
Twitter is a microblogging service in which the text of each message or tweet is up to 140 characters long. The tweet text can contain various elements including mentions of other users, hashtags, emoji, and URLs. What happens when a URL is included in the text of the tweet? Do the contents of the URL become part of the tweet? Can you understand the content and context of the tweet if you only read the text alone? Does the URL, in a way, extend the text of the tweet beyond 140 characters?Read More
Metaphors are magical. Unlike similes or analogs, which express a one-to-one mapping of concepts, metaphors are equivocal; they imply and suggest qualities that are not immediately apparent. Presented with a simile, we can accept or reject it. Offered a metaphor, we’re likely to respond with a “yes, but…” or with a quizzical “in what way do you mean this?” The mystery of the metaphor charms us into examining more closely what it is and how it fits into the context. A metaphor engages us; it invites us to think about how different aspects of the metaphor can create new meaning and understanding of the situation.
Metaphors are ambiguous. Through this ambiguity, they can provoke reflection, initiate discussions, and even provide a bridge to the unknown from something known. At their best, metaphors help us make sense of new situations and gain insight about the nature of disruptive events and evolving situations.
But at their worst, metaphors can constrain our thinking and limit our imagination. Instead of magic and engagement, a metaphor can be a sleight of hand that focuses our attention away from the critical actions and significant issues. This misdirection can happen implicitly without our awareness, as many metaphors have become so embedded in our discourse that we forget they are metaphors. References to space and vision are particularly evident in our everyday research discussions: I see what you mean [do we acknowledge that seeing is not comprehending?]; my area of research [deliberately bounding our interest and limiting our range of attention]; the information superhighway [directing us to consider that information and knowledge, as if they were in containers, move along predetermined paths]; etc.Read More
If you’ve read any of my previous posts you know that I am constantly experimenting with different ways to represent and explore social network data with R. For example, in previous posts I’ve written about sonification of tweet data, animation of dynamic twitter networks, and various ways to plot social networks (here and here). In each case the underlying idea is finding different ways to explore data under the assumption that sometimes just looking at something from a different point of view reveals something novel. In this post I will briefly discuss how to go from data to 3D model network, to 3D object using R most of the way.Read More
Finally and for the first time, Facebook released a report that allegedly discloses the global government requests for data. The report details (among other things) the name of country, the number of requests and the percentage of disclosed data by Facebook.
It is NICE that Facebook finally publishes a report about government data request. OK, it is important!
Google for example has been doing that regularly since 2009 with the Google Transparency Report. A report that is more detailed and gives more information (as it should) to users.
“Transparency and trust are core values at Facebook ” the report says. But now after Snowden-Gate, we know that certain governments (say the US) have direct access to data from Facebook and other big companies.
Shouldn’t Facebook disclose information about this as well? How much data and what kind of data is extracted on a daily basis by governments (say the US)? Of course Facebook is not the issue. The same request can be made to Google, Yahoo, Apple, Microsoft or name it.
Publishing a report and pretending it is transparency, is a good way to mask relevant information that should be accessible to users.Read More
What does a tweet sound like? Not the kind that flies around in the air, but the kind that zips to and from our mobile devices. I’m intensely interested in finding ways to make sense of data. Sonification of data – representing data with sound – offers one way to do that. This post steps through R code to take the text of tweets and turn them into short chirping sounds. It also uses different tones for different users so that each user has a “voice”. In other words, this post shows how to use R to make Twitter data sing.Read More
Robert Mason, Shawn Walker and Jeff Hemsley participated in the University of Washington, Information School’s iAffiliates Day, “an event that fosters new partnerships and showcases the innovative work being done at the iSchool. The event is an unconference format with the theme of discovering information partnerships”. Participants give a two minute lightning talk intended to “enlighten, inspire, educate, or otherwise engage the audience” about a given topic. Jeff chose to “otherwise engage” the audience with a two minute rap about data visualization. Read more for the full text.Read More
One difficulty with which social media researchers grapple is the separation of “noise” from “signal.” Noise is traditionally those data that don’t contain relevance to a given query – in this case, tweets about Occupy Wall Street, or more specifically, Occupy Oakland. Occupy Oakland took on the hashtag “#oo” fairly early into the occupations, which has served to create a headache for those of us at the SoMe Lab. Continuing my exploration into topic modeling and taking inspiration from digital humanists and practices of ‘pataphysics, I decided to explore the noise to see what it contains. After all, one person’s noise is another’s signal! Click through to read about #oo, emotions, tokenization, and linguistic difference and to see our first topic modeling visualization — and figure out what Ducktales has to do with #ows!Read More