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 http://nsmnss.blogspot.co.uk/

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 www.everydaysocialresearch.com. 

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: www.digital-mr.com/blog

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 http://blog.qsrinternational.com/

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: http://blog.qsrinternational.com/qualitative-data-analysis-with-nvivo/#sthash.8odh8Olf.dpuf

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 www.digital-mr.com/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 http://markcarrigan.net/. 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