Friday, 12 December 2014

NSMNSS event January 12th, London: Using social media for academic work - opportunities and challenges - book now

NSMNSS knowledge exchange event: Using social media for academic work - opportunities and challenges
Following on from the successful launch of our book of blogs on social media research, we will be holding a new knowledge exchange event on January 12th at NatCen in central London with two of the blog authors sharing their experiences of using social media for research. We're delighted to be joined by Dr Deborah Lupton, Centenary Research Professor at the University of Canberra, who is making a flying visit to the UK and also by Dr Luke Sloan from the COSMOS team at Cardiff University.

As well as the two main presentations there will be an opportunity for networking, lunch and the chance for you to share your research experiences in an open space forum where you can ask for advice and input on challenges or dilemmas you may be facing with your own research projects.

A draft agenda for the event is below; places are free but limited to 25 people so please book your place by clicking this link as soon as possible. 

If you would like to share your challenges, dilemmas or experiences on your own research during the informal open space session then email Kandy Woodfield at You won't need to prepare a presentation but we will ask you to talk about your research project or proposal and the issue you'd like input on for about 5 minutes.

Using social media for academic work - possibilities, benefits and risks
January 12th 2015, NatCen Social Research, 35 Northampton Square, London EC1V 0AX

10am             Arrival, tea & coffee and networking

10.30am       Using social media for academic work
Dr Deborah Lupton (@DALupton), University of Canberra

11.40am       Q&As

Midday         Open space forum

1pm              Lunch

1.45pm:         Investigating Social Phenomena Through COSMOS: A Case Study
                      of the  Horsemeat Scandal
Dr Luke Sloan, COSMOS team, Cardiff University

2.45pm:         Q&As

3pm:              Finish

Thursday, 11 December 2014

Share your ideas for tweetchat topics in the new year!

Do you have a burning question about social sciences and social media? Want to learn something specific from our cross-country, cross-discipline members? Now is your chance!

We are thinking ahead about the topics of our monthly tweetchats for 2015. Let us know in the comments what you want to discuss and learn more about and we will expand on the idea by coming up with questions to pose to the network to discuss.

Looking forward to your thoughts!

Wednesday, 10 December 2014

Missed out? Read the twitter feed from our chat on changing role of researchers in a social media world

See below for the twitter feed from our tweetchat on 9th December 2014 all about the changing role of researchers involved in online and social media research. Scroll to the bottom and work your way up to follow the conversation in the order it occured.

A summary of the 5 questions posed to those taking part is included below:

Q1: How is social media impacting upon you as a researcher? E.g. identity, work/life balance, ethics
Q2: How is social media changing our identities as researchers?
Q3 from : How does insider-outsider position influence your role, identity,access or objectivity?
Q4 from : What does it mean to be ‘virtually ethical’?
Q5: What are the key issues around social media for researchers going forward?
Q6: What topics shall we chat about on in the new year?

Wednesday, 19 November 2014

Making the most out of big data: computer mediated methods

Patrick Readshaw is a Media and Cultural Studies Doctoral Candidate at Canterbury Christ Church University. Patrick is interested in social media as an alternative and empowering source of information on current events, free from the constraints of other agenda-setting media forms. You can contact Patrick by email on  

When I was asked to write a blog for NSMNSS, I was certainly excited and being my first post of this kind I was suitably anxious about the prospect. However, my ongoing thesis has never ceased to provide interesting discussions with individuals in linked or parallel fields relating to social media. The main caveat in these discussions is that I often have to try not to over complicate things. With that in mind and my ham-fisted introduction out of the way I want to take some time to break down the value of so called “new media systems” like Twitter and the how I personally go about dealing with the data I collect. 

Since Social Media sites such as “Facebook” burst onto the scene 10 years ago, researchers and market analysts have been looking for a way to tap into the content on these sites. In recent years, there have been several attempts to do this with some being more successful than others (Lewis, Zamith & Hermida, 2013), particularly with regards to the scale of the medium in question. For those uninitiated (apologies to those that are) the term “Big Data” is the catch-all for the enormous trails of information generated by consumers going about their day in an increasingly digitized world (Manyika et al., 2011). It is this sheer volume of information that poses the first hurdle to be overcome when conducting research online. For example, earlier this year I was collecting data on the European Parliamentary Election and generated over 16,000 tweets in about three weeks. Bearing in mind that on average a tweet contains approximately 12 words in 1.5 sentences (Twitter, 2013), for those three weeks I had 196,500 words or 24,500 sentences to come to terms with. That is a lot of data for one person to deal with alone, especially if only applying manual techniques such as content analysis. 

So ultimately you have to ask two questions. Firstly how many undergraduates/interns chained to computers running basic content analysis is it going to take to complete the analysis in a reasonable space of time and whether that analysis is going to be reliable between the analysts. Secondly, while computational methods save time on analysis can you guarantee the same level of depth as with manual content analysis? Considering that content analysis goes beyond basic frequency statistics which can be collected simply from Twitter’s own search engine, I advocate the use of computer mediate techniques in which the data collected can firstly be reduced using filters to removes reTweets or spam responses and secondly to apply hierarchical cluster analysis among others to structure the data somewhat, or at least conceptualise it along a number of important factors. Both Howard (2011) and Papacharissi (2010) utilise this mixed methods approach as do Lewis, Zamith and Hermida (2013) whose method I adapted to my own work and applied as described above. Furthermore these individual pieces of research suggest the value of the medium overall as a source of data, due to its role as one of the primary news disseminators when access to mainstream news media is blocked such as during 2011 Arab Spring events. Burgess and Bruns (2012) have conducted addition research looking at the 2010 federal election campaign in Australia, advising the use of computational methods to reduce their sample to facilitate manual methods ultimately, maintaining depth during content analysis. As can be imagined Lewis, Zamith and Hermida (2013) and Manovich (2012) both support the methodologies utilized by the studies above and advocate making the most of the technical advances that have allowed for the content in question to be organized and harnessed in an efficient way.  

The application of mixed methodologies will continue to develop the techniques integral to facilitating the oncoming age of computational social science (Lazer et al., 2009) or “New Social Science”. While this is the case it is vitally important that while using this readily available source of data is not exploited in a way that could be potentially damaging to the medium as a whole and maintaining good research practice concerning the ethics associated with consumer privacy. As a final aside I would like to remind everyone that this data is hugely fascinating and rich beyond all belief but there are dangers associated with quantifying social life and if possible this should be at front of our minds before, during and after conducting research online (Boyd & Crawford, 2012; Oboler, Welsh & Cruz, 2012).


Boyd, d. & Crawford, K. (2012). Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15 (5), 662–679.

Burgess, J., & Bruns, A. (2012). (Not) the Twitter election: The dynamics of the #ausvotes conversation in relation to the Australian media ecology. Journalism Practice, 6 (3), 384– 402.
Howard, P. (2011). The digital origins of dictatorship and democracy: Information technology and political Islam. London, UK: Oxford University Press.

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barbási, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D. & Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science, 323 (5915), 721-723.

 Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content Analysis in an Era of Big Data: A Hybrid Approach to Computational and Manual Methods. Journal of Broadcasting & Electronic Media, 57 (1), 34–52.

Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the Digital Humanities (pp. 460–475). Minneapolis, MN: University of Minnesota Press.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

Oboler, A., Welsh, K., & Cruz, L. (2012). The danger of big data: Social media as computational social science. First Monday, 17 (7-2). Retrieved from

Papacharissi, Z. (2010). A private sphere: Democracy in a digital age. Cambridge, England: Polity Press.

Thursday, 13 November 2014

The changing nature of who produces and owns data: How will it impact survey research?

Brian Head is a research methodologist at RTI International. This post first appeared on SurveyPost on 20 May, 2014. You can follow Brian on Twitter @BrianFHead.

Cloud Photo

Survey researchers have become interested in big data because it offers potential solutions to problems we’re experiencing with traditional methods. Much of the focus so far has been on social media (e.g., Tweets), but sensors (wearable tech) and the internet of things (IoT) are producing an increasingly rich, complex, and massive source of data. These new data sources could lead to an important change in how individuals see the data collected about them, and thus have ramifications for those interested in gathering and analyzing those data.

Who compiles data?

Quantitative data about people have been gathered for millennia. But with technological advances and identification of new purposes for it, the past 100 years have seen significant increases in the amount of data produced and collected—e.g., data on consumer patterns and other market research, probability surveys, etc.

Common to these data are three factors: 1) the data are a commodity compiled, used, or traded by third parties; 2) generally there are no direct benefits to individuals about whom data are gathered; and 3) the organizations interested in the data gather, store, and analyze it. All this is not to say that throughout history individuals haven’t collected information about themselves. Individuals have collected qualitative data in the form of diaries and biographies. And, they have collected some quantitative data but this has generally to satisfy a third-party (e.g., collecting financial information to file taxes). But, now in addition to all of the data others compile about them, new technologies like wearable technologies (sensors) and IoT devices allow people to voluntarily produce and compile massive amounts of data about themselves and doing so can have a direct benefit to them. (Involuntary data collection through connected devices is already taking place—e.g., internet connected devices are being used for geo-targeting advertising).

Who owns or controls data?

Data are collected in different ways. Census data are collected periodically (intervals vary by nation) through a mandatory government data collection. Surveys generally operate under the requirement of voluntary participation, although there are exceptions.  Much of the consumer data gathered now is done surreptitiously. Examples include browser cookies that collect information about the websites we visit, search engines that collect information about the internet searches people conduct, email providers that scan emails, and apps that use geodata to market goods and services to prospective clients.

It seems the public is increasingly aware of and concerned with the sum of these data collections. According to a recent Robert Wood Johnson Foundation (RWJF) study large majorities of self-tracking app/device users think (84%) they do or want (75%) to own data that are collected with the device. There have been attempts to limit data collection, such as the recent attempt to limit the data the U.S. government collects on citizens.  Advocates of efforts like this tend to cite concerns over burden and privacy. The exponential growth of data collected both voluntarily and involuntarily through apps, sensors, and the IoT may cause similar (perhaps successful) attempts to change government and corporate policies to provide individuals more control over their data. In fact, market researchers are already beginning to respond to such an interest among consumers by offering to pay consumers for access to their browsing history, social network activity, and transactions they conduct online while at the same time giving those consumers control over which data they sell to the brokers.
As the amount of data collected about us increases, there’s a good chance individuals will increasingly see their data as their own, understand the value it has to various third parties, demand more control over it, and to be compensated for it. At first brush that may seem concerning. However, the type of compensation individuals’ desire for data will likely depend on how data will be used. For example, consumers are likely to continue to trade data for convenience in services (see thesis # 12). And, the RWJF report cited above suggests the usual leverages used to gain survey participation—e.g., topic salience and altruism—may work in gaining access to big data when the purpose of the study is for “public good research.”

Need for further research

Further research is needed in this area of big data to answer questions like: 1) to what extent, and how soon, will a larger proportion of the population begin to voluntarily use sensor and IoT devices; 2) will the general public continue to tolerate involuntary data collection when those data are collected by connected devices; 3) will the general public have opinions similar to early adopters in the RWJF about sharing personal data from connected devices with survey researchers; 4) will the leverages that work for gaining survey participation work for gaining access to personal big data or will new/additional leverages be needed; 5) will we be able to use techniques similar to those used to access administrative record data or will we need to develop new protocol for seeking permission to access these data? I look forward to seeing and contributing toward the research to answer these questions. What are your thoughts?

Thursday, 6 November 2014

You Are What You Tweet: An Exploration of Tweets as an Auxiliary Data Source

Ashley Richards is a survey methodologist at RTI International. This post first appeared on SurveyPost on 29, July 2014. 

Last fall at MAPOR , Joe Murphy presented the findings of a fun study he did with our colleague, Justin Landwehr, and me. We asked survey respondents if we could look at their recent Tweets and combine them with their survey data. We took a subset of those respondents and masked their responses on six categorical variables. We then had three human coders and a machine algorithm try to predict the masked responses by reviewing the respondents’ Tweets and guessing how they would have responded on the survey. The coders looked for any clues in the Tweets, while the algorithm used a subset of Tweets and survey responses to find patterns in the way words were used. We found that both the humans and machine were better than random in predicting values of most of the variables.

We recently took this research a step further and compared the accuracy of these approaches to multiple imputation, with the help of our colleague Darryl Creel. Imputation is the approach traditionally used to account for missing data and we wanted to see how the nontraditional approaches stack up. Furthermore, we wanted to check out these approaches because imputation cannot be used in the case where survey questions are not asked. This commonly occurs because of space limitations, the desire to reduce respondent burden, or other factors. I will be presenting on this research at the upcoming Joint Statistical Meetings (JSM), in early August. I’ll give a brief summary here, but if you’d like more details on it please check out my presentation or email me for a copy of the paper.

Income was the only variable for which imputation was the most accurate approach, but the differences between imputation and the other approaches were not statistically significant. Imputation correctly predicted income 32% of the time, compared to 25% for human coders and 26% for the machine algorithm. Considering that there were four income categories and a person would have a 25% chance of randomly selecting the correct response, I am unimpressed with these success rates of 25%-32%.

Human coders outperformed imputation on the other demographic items (age and sex), but imputation was more accurate than the machine algorithm. For these variables, the human coders picked up on clues in respondents’ Tweets. I was one of the coders and found myself jumping to conclusions, but I did so with a pretty good rate of success. For instance, if a Tweeter said “haha” a lot or used smiley faces, I was more likely to guess the person was young and/or female. These are tendencies that I’ve observed personally but I’ve read about them too.

As a coder I struggled to predict respondents’ health and depression statuses, and this was evident in the results. Imputation was better than humans at predicting these, but the machine algorithm was even more accurate. The machine was also best at predicting who respondents voted for in the previous presidential election, with human coders in second place and imputation in last place. As a coder I found that predicting voting was fairly simple among the subset of respondents who Tweeted about politics. Many Tweeters avoided the subject altogether, but those who Tweeted about politics tended to make it obvious who they supported.

So what does this all mean? We found that even with a small set of respondents, Tweets can be used to produce estimates with accuracy in the same range or better[1] as imputation procedures. There is quite a bit of room for improvement in our methods that could make them even more accurate. For example, we could use a larger sample of Tweets to train the machine algorithm and we could select human coders who are especially perceptive and detail-oriented. The finding that Tweets are as good or better as imputation is important because imputation cannot be used in the case where survey questions were not asked.

As interesting as these findings may be, they need to be taken with a grain of salt, especially because of our small sample size (n=29).[2] Relying on Twitter data is challenging because many respondents are not on Twitter, and those who are on Twitter are not representative of the general population and may not be willing to share their Tweets for these purposes. Another challenge is the variation in Tweet content. For example, as I mentioned earlier, some people Tweet their political views while others stay away from the topic on Twitter.

Despite these limitations, Twitter may represent an important resource for estimating values that are desired but not asked for in a survey. Many of our survey respondents are dropping clues about these values across the Internet, and now it’s time to decide if and how to use them. How many clues have you dropped about yourself online? Is your online identity revealing of your true characteristics?!?

[1] Even if approaches using Tweets may be more accurate than imputation, they require more time and money and in many cases may not be worth the tradeoff. As discussed later, these findings need to be taken with a grain of salt.

[2] We had more than 2,000 respondents, but our sample size for this portion of the study was greatly reduced after excluding respondents who don’t use Twitter, respondents who did not authorize our use of their Tweets, and respondents whose Tweets were not in English. Furthermore, half of the remaining respondents’ Tweets were used to train the machine algorithm.

Thursday, 30 October 2014

Innovations in knowledge sharing: creating our book of blogs

Kandy Woodfield is the Learning and Enterprise Director at NatCen Social Research, and the co-founder of the NSMNSS network. You can reach Kandy on Twitter @jess1ecat.

Yesterday the NSMNSS network published its first ebook, a collection of over fifty blogs penned by researchers from around the world who are using social media in their social research. To the best of our knowledge this is the first book of blogs in the social sciences.  It draws on the insights of experienced and well-known commentators on social media research through to the thoughts of researchers new to the field.

Why did we choose to publish a book of blogs rather than a textbook or peer-reviewed article?

 In my view there is space in the academic publishing world for peer reviewed works and self-published books. We chose to publish a book of blogs rather than a traditional academic tome because we wanted to create something quickly which reflected the concerns and voices of our members. Creating a digital text, built on people’s experiences and use of social media seemed an obvious choice. Many of our network members were already blogging about their use of social media for research, for those who weren’t this was an opportunity to write something short and have their voices heard.

Unlike other fields of social research,  social media research is not yet populated with established authors and leading writers, the constant state of flux of the field means it is unlikely to ever settle in quite the same way as ethnography say or survey research. The tools, platforms and approaches to studying them are constantly changing. In this context works which are published quickly to continue to feed the plentiful discussions about the methods, ethics and practicalities of social media research seem an important counterpoint to more scholarly articles and texts.

How did we do it?

Step 1 – Create a call for action: We used social media channels to publicise the call for authors, posting tweets with links to the network blog which gave authors a clear brief on what we were looking for. Within less than a fortnight we had over 40 authors signed up.

Step 2 -  Decide on the editorial control you want to have: We let authors know that we were not peer reviewing content, if someone was prepared to contribute we would accept that contribution unless it was off theme. In the end we used every submitted blog with one exception. This was an important principle for us, the network is member-led and we wanted this book to reflect the concerns of our members not those of an editor or peer-review panel. The core team at NatCen undertook light touch editing to formatting and spelling but otherwise the contributions are unadulterated. We also organised the contributions into themes to make it easier for readers to navigate.

Step 2 – Manage your contributions: We used Google Drive to host an author’s sign-up spreadsheet asking for contact information and also an indication of the blog title and content. We also invited people to act as informal peer reviewers. Some of our less experienced authors wanted feedback and this was provided by other authors. This saved time because we did not have to create a database ourselves and was invaluable when it came to contacting authors along the way.

Step 3 – Keep a buzz going and keep in touch with authors: We found it important to keep the book of blogs uppermost in contributors minds, we did this through a combination of social media (using the #bookofblogs) and regular blogs and email updates to authors.

Step 4 – Set milestones: we set not just an end date for contributions but several milestones along the way tgo achieve 40% and 60% of contributions, this helped keep the momentum going.

Step 5 – Choose your publishing platform: there are a number of self-publishing platforms. We chose to use Press Books which has a very smooth and simple user interface similar to many blogging tools like Wordpress. We did this because we wanted authors to upload their own contributions, saving administrative time. By and large this worked fine although inevitably we ended up uploading some for authors and dealing with formatting issues!

Step 6 – Decide on format and distribution channels - You will need to consider whether to have just an e-book, an e-book and a traditional book and where to sell your book. We chose Amazon and Kindle (Mobi) format for coverage and global reach but you can publish into various formats and there are a range of channels for selling your book. 

Step 7 – Stick with it… when you’re creating a co-authored text like this with multiple authors you need to stick with it, have a clear vision of what you are trying to create and belief that you will reach your launch ready to go. And we did, we hope you enjoy it.

Watch a short video featuring a few of the authors from the Book of Blogs discussing what their pieces are about, here
Join the conversation today; Buy the e-book here!

Tuesday, 21 October 2014

It started with a tweet...


Kandy Woodfield is the Learning and Enterprise Director at NatCen Social Research, and the co-founder of the NSMNSS network. You can reach Kandy on Twitter @jess1ecat.

It started with a tweet, a blog post and a nervous laugh. Three months later I found  myself looking at a book of blogs. How did that happen?! Being involved in the NSMNSS network since its beginning has been an ongoing delight for me. It's full of researchers who aren't afraid to push the boundaries, question established thinking and break down a few silos. When I began my social research career, mobile phones were suitcase-sized and collecting your data meant lugging a tape recorder and tapes around with you. That world is gone, the smartphone most of us carry in our pockets now replaces most of the researcher's kitbag, and one single device is our street atlas, translator, digital recorder, video camera and so much more. Our research world today is a different place from 20 years ago, social media are common and we don't bat an eyelid at running a virtual focus group or online survey. We navigate and manage our social relationships using a plethora of tools, apps and platforms and the worlds we inhabit physically no longer limit our ability to make connections.

Social research as a craft, a profession, is all about making sense of the worlds and networks we and others live in, how strange would it be then if the methods and tools we use to navigate these new social worlds were not also changing and flexing.  Our network set out to give researchers a space to reflect on how social media and new forms of data were challenging conventional research practice and how we engage with research participants and audiences. If we had found little to discuss and little change it would have been worrying, I am relieved to report the opposite, researchers have been eager to share their experiences, dissect their success at using new methods and explore knotty questions about robustness, ethics and methods.

Our forthcoming  book of blogs is our members take on what that changing methodological world feels like to them, it's about where the boundaries are blurring between disciplines and methods, roles and realities. It is not a peer reviewed collection and it's not meant to be used as a text book, what we hope it offers is a series of challenging, interesting, topical perspectives on how social research is adapting, or not, in the face of huge technological and social change.

We are holding a launch event on Wednesday 29th October at NatCen Social Research if you would like more details please contact us.

I want to thank every single author from the established bloggers to the new writers who have shared their thoughts with us in this volume. I hope you enjoy the book as much as I have enjoyed curating it. Remember you can follow the network and join in the discussion @NSMNSS, #NSMNSS or at our

Thursday, 16 October 2014

Analytics, Social Media Management and Research Impact

Sebastian Stevens is an Associate Lecturer and Research Assistant at Plymouth University. He teaches research methods to social science students specialising in quantitative methods. He is on twitter @sebstevens99 and has a blog site at 

A key benefit that social media can bring to social science research is through impact and engagement. Demonstrating how a research project will achieve impact and engage the public is a key requirement of most social science research bids today, with many funders looking for more than the traditional conference and journal article as being sufficient. Funders today want to see not only how your research will contribute to the current body of knowledge, but also how your research could impact other areas of academia as well as providing public engagement and economic and societal wide benefits.

To promote your research to the widest possible audience, it is often necessary to use a number of Social Media platforms in order to access different populations. It is also now possible to measure this level of engagement through the use of web analytics with the two most common social media platforms (Facebook and Twitter) both providing free access to analytic software for their users. Managing the content and evaluating the impact of a number of social media platforms can however become tiresome and laborious, an issue overcome by the use of a Social Media Management System (SMMS).

The benefits of using a SMMS are vast and take the hassle out of managing multiple social media platforms for your research for a reasonable yearly subscription. There are many SMMS on the market today with an example that I am currently using on a project being Hootsuite. This particular SMMS provides a research team the benefits of:

1.    Scheduling – Researchers are busy people and have little time to manage multiple social media accounts. With a SMMS you can schedule posts to be sent to multiple social media platforms at times of the day known to deliver the largest impact.

2.    Enhanced analytics – The standard analytics of the accounts included in the SMMS are available in one place, alongside extra features including Google Analytics and Klout scores.  

3.    Streams – These provide the opportunity to keep up to date with features of your accounts such as your newsfeeds, retweets, mentions, hashtag usage plus many others.

4.    Multiple Authors – Multiple authors can be added to the system taking the responsibility away from one member of the team.

5.    RSS/Atom feeds – You can keep up with updates of other websites related to your research by adding the RSS/Atom feeds to the system.

By adopting the use of a SMMS a research team has a centralised, hassle free dashboard in which to create and post content alongside evaluating its impact. Each management system comes at a different price and includes different features, however most will take the hassle out of managing your social media platforms and provide greater opportunities to evaluate your research impact.




Thursday, 9 October 2014

Sentiment And Semantic Analysis

Michalis founded DigitalMR in 2010 following a corporate career in market research with Synovate and MEMRB since 1991. This post was first published on the DigitalMR blog. Explore the blog here:

It took a bit longer than anticipated to write Part 3 of a series of posts about the content proliferation around social media research and social media marketing. In the previous two parts, we talked about Enterprise Feedback Management (December 2013) and Short -event-driven- Intercept Surveys (February 2014). This post is about sentiment and semantic analysis: two interrelated terms in the “race” to reach the highest sentiment accuracy that a social media monitoring tool can achieve. From where we sit, this seems to be a race that DigitalMR is running on its own, competing against its best score.
The best academic institution in this field, Stanford University, announced a few months ago that they had reached 80% sentiment accuracy; they since elevated it to 85% but this has only been achieved in the English language, based on comments for one vertical, namely movies -a rather straight-forward case of: “I liked the movie” or “I did not like it and here is why…”. Not to say that there will not be people sitting on the fence with their opinion about a movie, but even neutral comments in this case, will have less ambiguity than other product categories or subjects. The DigitalMR team of data scientists has been consistently achieving over 85% sentiment accuracy in multiple languages and multiple product categories since September 2013; this is when a few brilliant scientists (engineers and psychologists mainly) cracked the code of multilingual sentiment accuracy!
Let’s dive into sentiment and semantics in order to have a closer look on why these two types of analysis are important and useful for next-generation market research.
Sentiment Analysis
The sentiment accuracy from most automated social media monitoring tools (we know of about 300 of them) is lower than 60%. This means that if you take 100 posts that are supposed to be positive about a brand, only 60 of them will actually be positive; the rest will be neutral, negative or irrelevant. This is almost like the flip of a coin, so why do companies subscribe to SaaS tools with such unacceptable data quality? Does anyone know? The caveat around sentiment accuracy is that the maximum achievable accuracy using an automated method is not 100% but rather 90% or even less. This is so, because when humans are asked to annotate sentiment to a number of comments, they will not agree at least 1 in 10 times. DigitalMR has achieved 91% in the German language but the accuracy was established by 3 specific DigitalMR curators. If we were to have 3 different people curate the comments we may come up with a different accuracy; sarcasm -and in more general ambiguity- is the main reason for this disagreement. Some studies (such as the one mentioned in the paper Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews) of large numbers of tweets, have shown that less than 5% of the total number of tweets reviewed were sarcastic. The question is: does it make sense to solve the problem of sarcasm in machine learning-based sentiment analysis? We think it does and we find it exciting that no-one else has solved it yet.
Automated sentiment analysis allows us to create structure around large amounts of unstructured data without having to read each document or post one by one. We can analyse sentiment by: brand, topic, sub-topic, attribute, topic within brands and so on; this is when social analytics becomes a very useful source of insights for brand performance. The WWW is the largest focus group in the world and it is always on. We just need a good way to turn qualitative information into robust contextualised quantitative information.
Semantic Analysis
Some describe semantic analysis as “keyword analysis” which could also be referred to as “topic analysis”, and as described in the previous paragraph, we can even drill down to report on sub-topics and attributes.
Semantics is the study of meaning and understanding language. As researchers we need to provide context that goes along with the sentiment because without the right context the intended meaning can easily be misunderstood. Ambiguity makes this type of analytics difficult, for example, when we say “apple”, do we mean the brand or the fruit? When we say “mine”, do we mean the possessive proposition, the explosive device, or the place from which we extract useful raw materials?
Semantic analysis can help:
  • extract relevant and useful information from large bodies of unstructured data i.e. text.
  • find an answer to a question without having to ask anyone!
  • discover the meaning of colloquial speech in online posts and
  • uncover specific meanings to words used in foreign languages mixed with our own
What does high accuracy sentiment and semantic analysis of social media listening posts mean for market research? It means that a 50 billion US$ industry can finally divert some of the spending- from asking questions to a sample, using long and boring questionnaires- to listening to unsolicited opinions of the whole universe (census data) of their product category’s users.
This is big data analytics at its best and once there is confidence that sentiment and semantics are accurate, the sky is the limit for social analytics. Think about detection and scoring of specific emotions and not just varying degrees of sentiment; think, automated relevance ranking of posts in order to allocate them in vertical reports correctly; think, rating purchase intent and thus identifying hot leads. After all, accuracy was the only reason why Google beat Yahoo and became the most used search engine in the world. 

Thursday, 2 October 2014

7 reasons you should read Qualitative Data Analysis with NVivo

Kath McNiff is a Technical Communicator at QSR. You can contact Kath on @KMcNiff. This post was originally published on the NVivo blog. You can read more by Kath and other NVivo bloggers by visiting

Somewhere on your computer there are articles to review and interviews to analyze. You also have survey results, a few videos and some social media conversations to contend with.

Where to begin?

Well, here’s one approach: Push a few buttons and bring everything into NVivo. Then dive head-first into your material and code the emerging themes. Become strangely addicted to coding and get caught up in a drag and drop frenzy. Then come up for air – only to be faced with 2000 random nodes and a supervisor/client demanding to know what it all means.

Or, you could do what successful NVivo users have been doing for the past six years – take a sip of your coffee and open Qualitative Data Analysis with NVivo.

This well-thumbed classic (published by SAGE) has been revised and updated by Pat Bazeley and co-author Kristi Jackson.

Here are 7 reasons why you should read it:

1. Pat and Kristi guide you through the research process and show you how NVivo can help at each stage. This means you learn to use NVivo and, at the same time, get an expert perspective on ‘doing qual’.
2. No matter what kind of source material you’re working with (text, audio, video, survey datasets or web pages)—this updated edition gives you sensible, actionable techniques for managing and analyzing the data.
3. The authors share practical coding strategies (gleaned from years of experience) and encourage you to develop good habits—keep a research journal, make models, track concepts with memos, don’t let your nodes go viral. Enjoy the ride.
4. The book is especially strong at helping you to think about (and setup) the ‘cases’ in your project—this might be the people you interviewed or the organizations you’re evaluating. Setting-up these cases and their attributes helps you to unleash the power of NVivo’s queries. How are different sorts of cases expressing an idea? Why does this group say one thing and this group another? What are the factors influencing these contrasts? Hey wait a minute, I just evolved my theme into a theory. Memo that.
5. If you’re doing a literature review in NVivo – chapter 8 is a gold mine (especially if you use NCapture to gather material from the web or if you use bibliographic software like EndNote.)
6. Each chapter outlines possible approaches, gives concrete examples and then provides step-by-step instructions (including screenshots) for getting things done. All in a friendly and approachable style.
7. This book makes a great companion piece to Pat’s other new text – Qualitative Data Analysis Practical Strategies. Read the ‘strategies’ book for a comprehensive look at the research process (in all its non-linear, challenging and exhilarating glory) and read this latest book to bring your project to life in NVivo. - See more at:

Thursday, 25 September 2014

Thinking, Fast and Slow: The Social Media Research Perspective

Dr. Nicos Rossides is the CEO of Medochemie, an international pharmaceutical company with more than 100 operations worldwide. Nicos is also the Chairman of DigitalMR's Advisory Board. This post originally appeared on the Digital MR blog

Nobel laureate Kahneman has written a seminal book on the different types of thinking processes we humans deploy. In “Thinking, Fast and Slow” he argues that cognitive biases profoundly affect our daily decisions - from which toothpaste to buy, to where we should go on holiday. He goes on to claim that our decision processes can be understood only by knowing how two different thinking systems shape the way we judge and decide:

"System 1" is fast, instinctive, subconscious and emotional;
"System 2" is slower, deliberative, logical.
The book delineates cognitive biases, such as how we frame choices, loss aversion, and our tendency to think that future probabilities are altered by past events..  All these can throw light on fascinating facets of human judgement and thought and are posited by Kahneman to be both systematic and predictable.
Framing: Drawing different conclusions from the same information, depending on how or by whom that information is presented.
Loss aversion:  The disutility of giving up an object is greater than the utility associated with acquiring it.
Gambler’s fallacy: The tendency to think that future probabilities are altered by past events, when in reality they are unchanged.
How is this relevant to research? And why exactly should research agencies and their clients care? Well, I would argue that the basic dichotomy described in the book is critical to the existence of the market research industry. Our ability to generate insights, which in turn can only be gained through the analysis and interpretation of evidence, is key to managing a modern business – it is “system 2” thinking. Of course, one could get some things right by merely relying on intuition or gut-feeling; therein lies the caveat: get some things right. Indeed, chances are that the odds would be heavily stacked against you if you ignore facts and rely on less than rigorous or no analysis. Putting this in a different way, fast thinking is not a good way to raise your metaphorical batting average as a business.
One could certainly argue (correctly) that intuition does not occur in a vacuum – in that it often has its roots in prior experience. But testing your assumptions before going ahead with a decision is a way to avoid mistakes. Indeed, examining available evidence to inform decisions is a tried and tested way of succeeding in business. Ask Procter & Gamble  which spends hundreds of millions every year on painstakingly researching all aspects of the marketing mix.
We can draw a parallel to P&G’s B2C decision model  (based on “System 1” thinking) in which they established the “First moment of truth” which stated that there were 3-7 seconds from when the customer sees the stimulus to when they react (decide to buy). There was then a second decision (“Second moment of truth”) that customers made after the purchase; based on the negative or positive experience with the product, a decision would then be made as to whether they should continue using it/buy from this vendor again
Stimulus -> Shelf (First moment of truth) -> Self experience (Second moment of truth)
Google pointed out that the ubiquity of internet access has caused an upward trend in people (in a B2C and B2B context) after they had observed the stimulus, researching about the product/service to obtain more information before they made their first moment of truth decision; they call it the “Zero moment of truth” (ZMOT)
Stimulus -> Information (Zero moment of truth) -> Shelf (First moment of truth) -> Self experience (Second moment of truth)
Companies are now looking to the web as a solution; this should be done carefully as even using the web as a source of informing decision can lead to systematic biases (searching for what you want to see).
By analysing the zero moment of truth (sources from which customers are obtaining their information) and the first and second moments of truth of current/potential customers, (through what customers are saying online), DigitalMR serves to create an objective way to inform choices (the zero moment of truth) of decision makers of a company through tools such as social media listening, online communities and an array of other digital research tools.
 DigitalMR & ZMOT
So, fast thinking is all nice and good, deeply steeped in our evolutionary past, but when it comes to business, “system 2” slow thinking based on informed choices is the way to go, especially when dealing with big ticket decisions.
What is your view on systems 1 and 2 thinking? How many of your decisions are rooted in system 1 Vs system 2 Vs both? Please share your way of being part of the conversation during the zero moment of truth.

Thursday, 18 September 2014

Save the dates! Upcoming tweet chats

There have been lots of interesting discussions and topics floating around about new social media in the social scienes. What better way to share than to host some tweet chats! See below for the dates and the topic we will cover for each tweet chat. All times are London time.

Tuesday 7th October, 2014 at 5pm: Representativeness of online samples

Including: What are the geographical inequalities in contributions across different social media platforms? What approaches can we take to address this? How can we weight twitter data? How can we learn about demographics of people on social media, such as age, gender, employment?

Monday 17th November at 5pm: Ready, set, research!: accessing funds and data

Including: You have an idea for a study, how do you go about funding it? What funding streams are available? What are the regulations/restrictions of accessing different streams? How do we get our hands on big data sets from the likes of Google and Twitter?

Tuesday 9th December at 5pm: The changing role of researchers of SM

Including: How is social media changing our identities as researchers, as people? How does this effect our work? How does this impact the field of social sciences?

Remember to include #NSMNSS in all your posts to help us capture all of the discussion. We will provide a transcript of the Tweetchat on our blog following the event.

Wednesday, 17 September 2014

The future of social science blogging in the UK

Mark Carrigan is a sociologist and academic technologist and first wrote this blog post for his blog Contact Mark on Twitter @mark_carrigan

Earlier this week, NatCen Social Research hosted a meeting between myself, Chris Gilson (USApp, @ChrisHJGilson), Cristina Costa and Mark Murphy (Social Theory Applied, @christinacost & @socialtrampos ), Donna Peach (PhD Forum,Donna_Peach) and Kelsey Beninger (NSMNSS, @KBeninger) to discuss possible collaborations between social science bloggers in the UK and share experiences about developing and sustaining social science blogs over time. We didn’t do as much of the latter as I expected, though I personally found it valuable simply to voice a few concerns I’d had in mind about the direction of academic blogging that I’d heretofore been keeping to myself for a variety of reasons. The manner in which the audience for Sociological Imagination seems to have stopped growing over the last couple of years (unless I make an effort to tweet more links to posts in the archives) had left me wondering why I’d been operating under the assumption that the audience for a blog should be growing. I realise that I’d been working on the premise that an audience is either growing or it’s shrinking which, once I articulated it, came to seem obviously inaccurate to me. Considering this also raised questions about overarching purposes which I was keen to get other people’s perspectives on: what was the website for? To be honest I’m not entirely sure. After four years, it’s largely become both habit and hobby. It’s an enjoyable diversion. It’s a justification for spending vast quantities of time reading other sociology blogs. I’m invested in it as a cumulative project, such that even if I stopped enjoying it, I’d probably feel motivated to continue. I’m still preoccupied by how genuinely global it has become, something which feels valuable in and of itself. I’ve also had enough positive feedback at this point (I never know quite how to respond when people send ‘thank you’ e-mails but they’re immensely appreciated!) that all these other factors, essentially constituting its value for me, find themselves reflected in a sense that it’s clearly valuable for (some) other people as well.

Much of the early discussion at the meeting was about the limitations of metrics. It’s sometimes hard to know what to do with quantitative metrics of the sort that are so abundantly supplied by social media. What do they actually mean? Other people have seemingly had the same experience I’ve had of being provoked by these stats to wonder about what isn’t being measured e.g. if x number of people visit a post then how many people read the whole thing, let alone derive some value from it? We discussed the possibility of qualitative feedback, which is essentially what the aforementioned ‘thanks’ e-mails constitute, as something potentially more meaningful but difficult to elicit. Are there ways to pursue qualitative feedback from the audience of a blog? Cristina and Mark described their current project aiming to use an online questionnaire to get information about how Social Theory Applied is seen by readers and how the material is being used. Are there others ways to get this kind of feedback? Perhaps I should just ask on the @soc_imagination twitter feed? I guess the thing that makes me uncomfortable is the risk of slipping into a publisher/consumer orientation, given this is a relation so well established in contemporary society – I don’t see the people reading the site as consumers and I don’t see myself as a publisher. In fact I’ve found it immensely frustrating on a few occasions when I’ve felt people adopt the mentality of a consumer with me e.g. leaving a comment that “there’s no excuse for posting a podcast with such low audio quality” or “why haven’t you fixed the broken link on this [old] post?”. While I’d like to get qualitative feedback on Sociological Imagination, particularly more of a sense of how people use material on the site if it’s for anything other than momentary distraction, I basically have no intention of doing anything other than what I want with it, as well as leaving the Idle Ethnographer as my co-editor to do the same.

We also discussed a range of potential collaborations which we could pursue in future. One of my concerns about the general direction of social science blogging in the UK is that the LSE blogs and the Conversation might gradually swallow up single-author blogs – in the case of the former, the fact they often repost from individual blogs mitigates against this but I think there’s still a risk that single author blogging becomes a very rare pursuit over time, simply because it’s difficult to sustain it and build an audience while subject to many other demands on your time. I think the likelihood of this happening is currently obscured by academic blogging becoming, at least in some areas, slightly modish, in a way that distracts from the question of whether new bloggers are likely to sustain their blogging in a climate where their likely expectations are unlikely to be met by the activity itself. I like the idea of finding ways to share traffic and I suggested that we could experiment with aggregation systems of various sorts: perhaps framed as a social science blogging directory which people apply to join, at which point their RSS feed is plugged into a twitter feed that automatically aggregates all the other blogs on the list. Another possibility would be to use RebelMouse to create what could effectively be a homepage for the UK social science blogosphere (in the process perhaps bringing this blogosphere into being, as opposed to it simply being an abstraction at present). Chris Gilson suggested the possibility of creating a shared newsletter in which participating sites included their top post each week or month, in order to create a communal mailing which profiled the best of social science blogging in the UK. Despite being initially antipathetic towards it, this idea grew on me as I pondered it on the way home – not least of all because it could be a way to connect with audiences who are unlikely to read blogs on a regular basis. However while it would be easy to create prototypes of any of these to test the concept, it’s less obvious how they would work on an ongoing basis. The latter two would require a small amount of funding and/or someone willing to take on an unpaid task. Perhaps more worryingly from my point of view as someone who goes out of my way to avoid formal meetings in general and those concerned with elaborating procedures in particular, it seems obvious to me that some filtering criteria would be required (e.g. should blogs have to be continued past a certain point to join the aggregator? should there be quality criteria and, if so, who would assess them?) to ‘add value’ but I have no idea what these would be nor do I see how they could be fairly elaborated without a long sequence of face-to-face meetings that would likely prove tedious for all concerned. Perhaps I’m being overly negative, particularly since two of the ideas were my own, but I don’t see the point of writing a ‘reflection’ post like this and not being upfront about where I’m coming from.

We also discussed the possibility of longer term collaborations. Would social science blogging in the UK benefit from something like The Society Pages and, if so, how do we go about setting it up? I cautioned against overestimating the possible benefits of the umbrella identity TSP provides but I really have no idea. We discussed whether we should talk to the editors of the site in order to learn more about their experiences. I can certainly see the value in pursuing something like this and, as with the aggregators, it has the virtue of facilitating collaboration while retaining the individual identities of the participating sites – for both principled and practical reasons, I don’t want to collaborate in a way that dilutes the identity of the Sociological Imagination. Plus, even if I did, I’d have to ask the Idle Ethnographer and I suspect she feels even more strongly about this than I do. This discussion segued quite naturally into a broader question of how to fund academic blogging in the UK – framed in these terms, my initial ambivalence about pursuing funding melted away because I’d like nothing more than to find a way to fund blogging as an activity. My experiences at the LSE suggest this might be harder than it seems but we discussed this in terms of winning money to buy out people’s time to participate in these activities. I’ve always been an enthusiast for the LSE model of research-led editorship (as opposed to the journalist-led editorship of the Conversation, which I think leads to an often sterile product in spite of the faultless copy) so I’d like it if this possibility, as a distinctive occupational role in itself, doesn’t slip out of the conversation but it’s difficult for all sorts of reasons. I think it would also be beneficial to find ways of employing PhD students on a part-time basis, either for ad hoc assignments or work on an ongoing basis, given the retrenchment of funding and the congruence between the demands of a PhD and paid work of this sort. My one worry here is that the pursuit of funding undermines what I would see as the more valuable outcome of establishing blog editorship on an equivalent footing with journal editorship – given the latter does not, as far as I’m aware, factor into workload allocations anywhere, advocating that time for blog editing should be bought out risks preventing an equivalence between these two roles which I suspect would otherwise be likely to emerge organically over time.

My sense of the key issues facing the UK social science blogosphere:  
  • How to share experiences, allow practical advice to circulate and facilitate the establishment of best practice
  • Finding qualitative metrics to supplement the quantitative metrics provided by blogging platforms
  • Making it easier for new bloggers to build audiences and promote their writing
  • Experimenting with aggregation projects to help consolidate the blogosphere and share traffic
  • Finding ways to fund social science blogging (for students, doctoral researchers and academics)
  • Increasing the recognition of social science blogging as a valuable academic activity
  • Ensuring that social science blogging remains a researcher-led activity and doesn’t get subsumed into institutionalised public engagement schemes
  • Encouraging the development of group blogs as a type distinct from single-author blogs and multi-author blogs with designated editors

Tuesday, 9 September 2014

A student perspective on 'Qualitative Online Interviews' by Dr Janet Salmons

Ivett Ayodele is an undergraduate student at the University of Salford studying BSc (Hons) Psychology and Counselling. She tweets as @ivettayo and @salfordpcy1 and blogs here.  

I have accepted the challenge to review Qualitative Online Interviews by Dr Janet Salmons (2015) because I believe that as part of the next generation of psychologists, it is a great opportunity to familiarise myself  with the emerging methods of online interviews, which will surely become popular in the future. It is also a great chance to extend my knowledge of qualitative methods generally, while writing this post helps to develop my academic writing skills. My task is to give a student perspective on the book since a lecturer’s perspective has already been explored (see here).

Qualitative Online Interviews by Dr Janet Salmons (2015) guides researchers and students through the process of extending their research into various online settings and it gives guidance on ethical issues that can arise during online interviews. As the author puts it, “the purpose of Qualitative Online Interviews is to encourage researchers to extend the reach of their studies by using methods that defy geographic boundaries” (Salmons, 2015, p. xviii). The book is structured around the E-Interview Framework, a conceptual system which helps to understand interrelationships between the key elements of Online Interviews and aids the process of decision making throughout the research design.

As a first year undergraduate student, I have had the opportunity to learn extensively about quantitative research methods; however Qualitative Online Interviews by Dr Janet Salmons (2015) gave me the opportunity to extend my growing knowledge of qualitative methods. This learning journey ‘forced’ me to develop a complex picture of research methods and now I have a better understanding of both quantitative and qualitative methods; while  the benefits of mixed methods became crystal clear to me.

Qualitative Online Interviews (Salmons, 2015) gives a deep insight into specific ethical issues surrounding online interviews. The author took the typical ethical issues of research, such as informed consent or confidentiality, and placed them at the heart of online interviews. For example Dr Janet Salmons draws attention to the possible flaws in data protection in an online setting by pointing out that some companies who own the platform, where the data is stored, might not have adequate protection against unauthorized access.

The cover and the design of the book reminded me of my old school books; however I found that the simple design helped me to focus more on the text, rather than on the pictures and tables. I found this useful, especially as I was learning new concepts. For example, taking a position as an insider (EMIC) or outsider (ETIC) researcher was a new concept which helped me to appreciate the possible design flaws of a qualitative study, as well as the richness of it, compared to a quantitative study.

The detailed content page and the organization of the book helps the reader to find exactly what they are looking for; yet I found that this book works for me best if I read it first from cover to cover.

 I found the Researcher’s Notebook section and Discussions and Assignments at end of each chapter very helpful. The Researcher’s Notebook section encouraged me to think about each concept as a practical issue and therefore made it easier to understand and relate concepts to research methods. For example in Chapter 3 -Choosing Online Data Collection Method and Taking a Position as a Researcher- Salmons (2015) explains the main ideas of the chapter through her previous studies, which made these concepts to come “alive”.

The Discussion and Assignment section facilitate further learning by raising some questions in regards to each concept. For example in Chapter 9 (Preparing for an Online Interview), Salmons (2015) talks about the importance of Epoche –“ to approach each interview with clear and fresh perspective”- subsequently the Discussion and Assignments part encourages students/researchers to talk/think through the Epoche concept and raises the question, what could be done to clear our mind before an online interview?

The accompanying website is not as user friendly as I would like, however once I found my way around it, I felt that it is a great way to extend the learning experience for students. The website contains of a general resources and a student resources part.

The general resources section offers materials such as course outline with suggested assignments, learning activities, worksheets and media pieces. They are great for academics for planning a course or seminar on qualitative online interviews and they are also useful for students who want to build on their knowledge outside the classroom. The student resource part is broken down into the chapters of the book. In each chapter students can find the definitions of new terms on e-Flashcards, which is a great learning tool. Students can choose whether they would like to see the term or the definition of the term and learn new terminology while they are having fun!

Qualitative Online Interviews by Dr Janet Salmons has not only extended my knowledge about qualitative methods and online interviews but it also deepened my knowledge about ethical issues during online and off-line research. I would recommend this book to any undergraduate student and if someone chooses to conduct online research for their dissertation, I believe that this book is a must have!


Monday, 25 August 2014

D-day minus 4 at #bookofblogs HQ

It's been a whirlwind summer, full of sport, sunshine, a bit of rain (at least here in the UK) and I hope you've had some memorable holidays... In amongst all that something pretty special has been happening at the #bookofblogs HQ. Over the last nine weeks researchers, practitioners, academics and bloggers have been penning their thoughts on the use of social media for research and sharing them with us.

Back in June when I wrote sometimes you have to 'just do it' and lobbed the idea of a crowd-sourced book of blogs into the Twittersphere, it wasn't without a fair bit of trepidation. Would anyone respond? What would people write about? Would we reach beyond existing bloggers and network members.

You know what, people (yes that's you!) really did respond. We've now got 40 uploaded blogs, just ten short of our target and whilst we've lost a few authors along the way (we'll miss you), we've gained others. And in keeping with our goal 'you write it & we'll publish it' the book is light on editing but packed full of fascinating insights, how to's, how not to's, opinion pieces, perspectives and personal journeys. We've got well respected academics, bloggers and first time writers rubbing shoulders with one another each bringing different perspectives & insights for us all to learn from.

It's been a brilliant effort. Thank you.

We're entering the final stages for contributions, there's a tiny clutch of blogs to arrive yet, just let us know if you need help uploading we so want to hit that 50 blog target. You can see the sheer diversity & richness of the contributions to date from this rough index:

So what's next? It's a final but crucial lap.  We're doing some organisation of the book into themes, checking it works as an ebook on different devices and then we'll be gearing up for launch. We're really hoping that we can have a proper launch party so we'll keep you posted on that but right now we need each of you to think how you can help publicise the book in the run up to launch.

Please keep watching and keep sharing, tell others about the book, let them know it's coming to an digital reader near them soon :) tweet, blog or carrier pigeon about it but please, please help us get the word out. We'll help you by providing some visual hooks and glimpses of content in the run up to the launch in late September, early October.

You've built it and now we want it to fly off the virtual bookshelf...