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 p.j.readshaw68@canterbury.ac.uk  

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).


References

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 http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3993/3269.

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

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.

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?

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!

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. 

Back to University: Summary of ‘Research Ethics into the Digital Age’ Conference

Kelsey Beninger is a researcher at NatCen Social Research.

Recently I was invited to speak at Sheffield University’s ‘Research Ethics into the Digital Age’ Conference. It was an exciting opportunity, not in the least because it involved a swanky pre-conference speaker’s dinner. But really, it was exciting because the University was celebrating ten years of its research ethics committee and was launching their new purpose-built online ethics submission portal. Paper-based applications, be gone!

Usually at these type of ethics and internet mediated research events there are a diverse bunch of cross discipline and cross institution technicians, practitioners, ethical specialists, to name a few. This event had a diverse audience but in a different way; they were mostly from Sheffield University. The great turnout demonstrated how there was not only commitment to high ethical standards, but actual interest from across departments and job roles at the University. I met a few administrators that manage the huge numbers of ethics applications, members of the university research ethics committee, and students and professors galore!

The morning had a great line up of key note speakers. Professor Richard Jenkins from Sheffield University provided a nice overview of ethics in international projects around three themes:

1. data must satisfy the host country’s legal and ethical requirements,
2. data must satisfy your university’s REC policy, and
3. data must satisfy the professional standards of the profession you are associated with.

Some obvious but important key points included knowing the specific cultural, legal and social laws of the country you are going to research in before you get there and keep a detailed paper trail of everything you do. Also, you share risks with any collaborators so if they don’t have governance framework in their country then discuss it early and encourage the application of your UK standards. The moral of the story: the ethical situation is more complex with international research but the responsibilities you have as a researcher with respect to ethics are the same.

Professor Joe Cannataci from the University of Malta cut the legal jargon and conveyed important points about data protection and the use of personal data from the internet. He drew on an intriguing array of international projects (one of which may have involved a funny story of him dancing his entrance to gain acceptance when meeting a rural tribe in Malaysia). He started his presentation by discussing the principle of relevance in data protection law. This is something many of us in research are familiar with- collect only the right data, collected only by the right people, at the right time and used by the right people in the right way for an agreed time. Say that 10 times fast! Of particular relevance to researchers beginning studies across Europe is knowing where the data is to be stored because there are different data protection laws in European countries compared to EU countries.

Next up was Claire Hewson from the Open University. Claire provided an overview of the challenges associated with the ethics of internet mediated research. A point that got me thinking ‘Is there truly an ‘unobtrusive’ type of data collection?’ was her distinction between obtrusive and unobtrusive methods. Obtrusive are activities such as actively recruiting individuals and those individuals knowingly partake in research. Unobtrusive methods included big data, data mining, and observations. It’s only unobtrusive because people are not aware of it in the first instance. It would become pretty obtrusive to some if participants were cognisant of what was being done with their personal data. A nice take away point from the presentation was that ‘thinking is not optional’ when it comes to applying ethical frameworks to changing online environments.

After a tasty lunch in Sheffield University’s lovely new student union building my turn was up! I delivered a session on the challenges of social media research, drawing upon recent exploratory research with users of social media (http://www.natcen.ac.uk/our-research/research/research-using-social-media-users-views/) about their views on researchers using their data in their work. I also drew on the work of the network and the survey conducted by network member Janet Salmons on researcher and practitioner views of the ethics of online research. My group explored the challenges associated with three themes: recruitment and data collection; interviewer identity and wellbeing; and analysis and presentation of data. Summarising key points that we as researchers are familiar with, I pushed the issues home by using direct examples from our exploratory research.

We wrapped up with a section on recommendations including only using social media in research if it is appropriate for your question; being transparent with participants and other researchers about risks of the research and the limitations of your sample; taking reasonable steps to inform users of your intention to utilise their data in research. Read the full list of recommendations in the report, here.

The contribution of social media to human resource management

Zhao, Tianzhang is a student in the Social Media MA at the University of Westminster.

Social media helped to generate energy and mobilize a community of support in the U.S. presidential election in 2008, which helped Barack Obama to achieve popularity (Jue, et al., 2010). According to Jue and his colleagues (2010), this fervor of political influence would be of particular value to any community activist, no matter their political beliefs or organizational affiliation.

If this were true what would this mean for organisational management? Most organisations seek to engage employees, clients, customers, suppliers and partners in an effort to achieve brand loyalty to their products and services. However, in today’s world political and business leaders cope with increasingly difficult circumstances in achieving these objectives (Jue, et al., 2010).

What to do? Well, social media is a useful platform for leaders to construct and share their strategic goals in a relatively efficient and accessible way. To gain and sustain competitive advantage, leaders need to rely on the engagement and commitment of those they work with, namely their employees and partners. They also could depend on social media platforms to accelerate and enhance employee innovation, engagement, and performance (Jue, et al., 2010); The elements of human resource management in an organization. In other words, social media

As Jue and his colleagues (2010: 2) claimed, “those who are actively using social media in their organizations can be confident that they have new ways to improve their business performance, create long-term capability, and ultimately sustain their success”.

Based on Jue and his colleagues’ work (2010: 74-75), social media would be a great help at work, which would be reflected in the following ways:

1.  Incorporated into a company’s corporate culture and critical to its strategy.
2. cost effective.
3. scales more effectively to meet a global audience’s training needs.
4. engages employees in sharing knowledge and expertise.

For example, we can look at the NHS. In practice, NHS states that social media helps them enhance their human resource efficiency. In their 2013 report on their employers, it was pointed out that firstly, social media offers a great platform for both organizations and individuals to listen and have conversations with people they want to influence and talk to. Secondly, social media provides an online platform for HR managers to highlight the working behavior guidance and HR policies. Thirdly, the next generation of NHS employees would rely on getting information from the internet and mobile devices, therefore, how NHS embraces these social media users for the benefits of employees and patients would be significant in developing a sustainable NHS. Finally, if NHS could trust their employees with the patients’ lives, why can employees not be trusted on social media?

To sum up, the relationship between social media and human resource management has an unexpected change in this dynamic environment. For social media researchers, it should be emphasized that the unexpected function of social media would always emerge along with the changing environment in specific industries or working areas. For HR managers and leaders, it is time to be aware of the importance of social media’s impact at work, and think about how to take the advantage of using social media effectively to develop the organization and promote business performance.

References

CIPD (Chartered Institute of Personnel and Development). (2013). The role of HR in corporate responsibility. Available: http://www.cipd.co.uk/binaries/6100%20SOP%20Corporate%20Responsibility%20(WEB).pdf. Last accessed 1st Dec 2013.

Jue, A.L., Marr, J.A. & Kassotakis, M.E. (2010). SOCIAL MEDIA AT WORK: How Networking Tools Propel Organizational Performance. United States of America: Jossey-Bass.

NHS. (2013). HR and social media in the NHS. Available: http://www.nhsemployers.org/Aboutus/Publications/Pages/HR-social-media-NHS.aspx. Last accessed 1st Dec 2013.

Blurring the limits between personal and professional life

María Belén Conti is a student in the Social Media MA at the University of Westminster.

As a journalist, I usually find myself in a difficult situation when it comes to social media: would it affect my job opportunities if I openly express my opinion of certain topics online as my friends do? Should I always be professional because if anything personal is filtered I will lose my credibility (major asset for a journalist)?

Some may say that the best solution is to have two different profiles, one for personal and other professional proposes (EFE, 2011; Restrepo, 2012). But again, the personal profile is there and the chance of information, opinions or photos filtering to audiences we don’t want to reach is still high.

“You have one identity...The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly... Having two identities for yourself is an example of a lack of integrity”, said Mark Zuckerberg (quoted in Meikle and Young, 2012, p.129). But is that really the case?

As Erving Goffman (1959) points out in his book The presentation of self in everyday life, we are always performing different roles to different audiences: “When an individual appears in front of others, he knowingly and unwillingly projects a definition of situation, of which a conception of himself is an important part” (p.234-235).

In other words, we won’t show the same persona in our work, in front of our family or with our friends. And that doesn’t make us lose our integrity, even if Zuckerberg doesn’t agree. Those different audiences have different expectations of us, and therefore we will highlight those aspects that better fit the “front” we want to show in each performance.

But, what happens when social and networked media mix those audiences? What if they get access to the back stage that we want to keep private? After all, as Goffman points out, usually we relax when we know we are not being watched. However, with the increased visibility online, those chances are reduced. As Meikle and Young (2012) explain, “convergent media make the invisible visible” (p.129). So that brings me back to the beginning of this post: how does that affect our lives? Are the limits between professional and personal life blurred?

To address this issue, the concept of Foucault’s Panopticon is useful. It implies that the permanent visibility make us modify our behaviour, being more cautious than what we would be if not being watched (Thompson, 1995; Meikle and Young, 2012). I find it interesting that a study among long-distance students (Bregman and Haythornthwaite, 2003) -whose assignments include regular blogposts - confirm that we pay more attention to what we say and how we say it when we know we are being observed and that our contributions may be searched later:

“Every opinion, however well expressed, every joke, turn of phrase, and typographical error remains preserved, leaving a written legacy of an individual’s persona and style” (p.124-125).

The same can be said about photos, videos and opinions we publish online. If something is on the Internet, you cannot be sure it won’t be filtered. Even if we have good management of our privacy settings, our friends may comment or share that post and they may have different privacy settings than ours. So if we don’t want to risk something becoming publically available perhaps we better not publish it anywhere on the Internet.

References:

Bregman, A. and Haythornthwaite, C.(2003). Radicals of presentation: visibility, relation, and co-presence in persistent conversation. New Media & Society, 5 (1), 117-140. [online] Available from: Sage Publications. < http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.90.6651&rep=rep1&type=pdf> [Accessed 30 November 2013]

EFE News Agency. (2011). Guía para empleados de EFE en redes sociales (Guide for EFE’s employees in social media). [online] Available from: <http://www.efe.com/FicherosDocumentosEFE/Gu%C3%ADaEFE-Redes.pdf> [Accessed 1 December 2013]

Goffman, E. (1959). The presentation of self in everyday life, Harmondsworth: Penguin

Meikle, G. and Young, S. (2012). Media convergence: networked and digital media in everyday life, Basingstoke: Palgrave Macmillan

Restrepo, H (2012). Borrador manual de estilo en redes sociales (Draft style guidelines for social media). [online] Available from: <http://es.scribd.com/doc/113601191/Borrador-Manual-de-Estilo-en-Redes-Sociales-Hernan-Restrepo> [Accessed 30 November 2013]

Thompson, J. (1995). The media and modernity: a social theory of the media, Cambridge: Polity Press

Leadership Opportunities at NSMNSS

We are inviting interested individuals to consider a range of exciting and flexible leadership opportunities to ensure the NSMNSS platforms remain active and informative to our 400+ members. Without your support we would be unable to continue this vibrant and engaged community of cross-discipline practitioners and researchers.

Please share, tweet, shout this news to anybody who you think may be interested in the opportunities outlined below.

Please get in touch by Monday, 2 September if you can commit to any of the opportunities described below, indicating your preference and confirming your availability to meet the requirements of the role. You can contact Jerome (jerome.finnegan@natcen.ac.uk) or Kelsey (kelsey.beninger@natcen.ac.uk).

Leadership Opportunities

• X1 Blog Editor
• Time Commitment: 3 hours a month, from September 2nd – January 1^st
• Key Role: Support the overall development of the blog by sourcing and publishing new posts, occasionally drafting your own post, co-ordinating the publishing timetable and liaising with the PhD bloggers, guest bloggers and the Twitter Account Manager.
• Requirements:
• Network member with an interest in the range of opportunities and challenges provided by new social media research.
• Write 1 new substantive blog a month (300-500 words)
• Source and post 1 new blog a week (300-500 words; could include event ad, event review, guest blog, sharing links)
• Prepare a short handover report documenting your time as Blog Editor, to support the next Blog Editor.

• X2 PhD Bloggers
• Time Commitment: 1 hour a month, from September 2nd – January 1^st
• Key Role: Publicise your research and share your experiences as a researcher of new social media in short blog posts, including the challenges you encounter, the solutions you develop, the tools you use and the topics you are researching.
• Requirements:
• PhD student
• X1 post every 4 weeks, between 300 and 500 words long
• Prepare a short handover report documenting your time as a PhD Blogger, to support the next PhD Blogger.

• x1 Twitter Manager
• Time Commitment: 1 hour a week, from September 2nd – January 1^st
• Key Role: To maintain and enhance the very popular NSMNSS twitter account.
• Requirements:
• Network member with an interest in the range of opportunities and challenges provided by new social media research.
• Login to NSMNSS twitter account at least x2 a week, respond to tweets and tweet no more than x3 tweets of new content a day.
• Respond to all tweets within 5 days.
• Develop, promote and host x1 tweetchat
• Prepare a short handover report documenting your time as Twitter Manager, to support the next Twitter Manager.

Available Support

Detailed written guidance for the Blog Editor, PhD Bloggers and Twitter Manager will be provided, and NatCen Social Research will remain a point of contact for these individuals should they have any questions or require support.

The above role descriptions are a suggestion; we are happy to discuss ideas for different ways of working.

Thank you. We look forward to hearing from you!