Easy availability of Twitter data links nicely to a key goal of computational social science. If researchers can find ways to impute user characteristics from social media, then the capabilities of computational social science would be greatly extended. However few papers consider the digital divide among Twitter users. But the question of who uses Twitter has major implications for research attempts to use the content of tweets for inference about population behaviour. Do Twitter users share identical characteristics with the population interest? For what populations are Twitter data actually appropriate?

A new article by Grant Blank published in Social Science Computer Review provides a multivariate empirical analysis of the digital divide among Twitter users, comparing Twitter users and nonusers with respect to their characteristic patterns of Internet activity and to certain key attitudes. It thereby fills a gap in our knowledge about an important social media platform, and it joins a surprisingly small number of studies that describe the population that uses social media.

Comparing British (OxIS survey) and US (Pew) data, Grant finds that generally, British Twitter users are younger, wealthier, and better educated than other Internet users, who in turn are younger, wealthier, and better educated than the offline British population. American Twitter users are also younger and wealthier than the rest of the population, but they are not better educated. Twitter users are disproportionately members of elites in both countries. Twitter users also differ from other groups in their online activities and their attitudes.

Under these circumstances, any collection of tweets will be biased, and inferences based on analysis of such tweets will not match the population characteristics. A biased sample can’t be corrected by collecting more data; and these biases have important implications for research based on Twitter data, suggesting that Twitter data are not suitable for research where representativeness is important, such as forecasting elections or gaining insight into attitudes, sentiments, or activities of large populations.

Read the full article: Blank, G. (2016) The Digital Divide Among Twitter Users and Its Implications for Social Research. Social Science Computer Review. DOI: 10.1177/0894439316671698

We caught up with Grant to explore the implications of the findings:

Ed.: Despite your cautions about lack of representativeness, you mention that the bias in Twitter could actually make it useful to study (for example) elite behaviours: for example in political communication?

Grant: Yes. If you want to study elites and channels of elite influence then Twitter is a good candidate. Twitter data could be used as one channel of elite influence, along with other online channels like social media or blog posts, and offline channels like mass media or lobbying. There is an ecology of media and Twitter is one part.

Ed.: You also mention that Twitter is actually quite successful at forecasting certain offline, commercial behaviours (e.g. box office receipts).

Grant: Right. Some commercial products are disproportionately used by wealthier or younger people. That certainly would include certain forms of mass entertainment like cinema. It also probably includes a number of digital products like smartphones, especially more expensive phones, and wearable devices like a Fitbit. If a product is disproportionately bought by the same population groups that use Twitter then it may be possible to forecast sales using Twitter data. Conversely, products disproportionately used by poorer or older people are unlikely to be predictable using Twitter.

Ed.: Is there a general trend towards abandoning expensive, time-consuming, multi-year surveys and polling? And do you see any long-term danger in that? i.e. governments and media (and academics?) thinking “Oh, we can just get it off social media now”.

Grant: Yes and no. There are certainly people who are thinking about it and trying to make it work. The ease and low cost of social media is very seductive. However, that has to be balanced against major weaknesses. First the population using Twitter (and other social media) is unclear, but it is not a random sample. It is just a population of Twitter users, which is not a population of interest to many.

Second, tweets are even less representative. As I point out in the article, over 40% of people with a Twitter account have never sent a tweet, and the top 15% of users account for 85% of tweets. So tweets are even less representative of any real-world population than Twitter users. What these issues mean is that you can’t calculate measures of error or confidence intervals from Twitter data. This is crippling for many academic and government uses.

Third, Twitter’s limited message length and simple interface tends to give it advantages on devices with restricted input capability, like phones. It is well-suited for short, rapid messages. These characteristics tend to encourage Twitter use for political demonstrations, disasters, sports events, and other live events where reports from an on-the-spot observer are valuable. This suggests that Twitter usage is not like other social media or like email or blogs.

Fourth, researchers attempting to extract the meaning of words have 140 characters to analyze and they are littered with abbreviations, slang, non-standard English, misspellings and links to other documents. The measurement issues are immense. Measurement is hard enough in surveys when researchers have control over question wording and can do cognitive interviews to understand how people interpret words.

With Twitter (and other social media) researchers have no control over the process that generated the data, and no theory of the data generating process. Unlike surveys, social media analysis is not a general-purpose tool for research. Except in limited areas where these issues are less important, social media is not a promising tool.

Ed.: How would you respond to claims that for example Facebook actually had more accurate political polling than anyone else in the recent US Election? (just that no-one had access to its data, and Facebook didn’t say anything)?

Grant: That is an interesting possibility. The problem is matching Facebook data with other data, like voting records. Facebook doesn’t know where people live. Finding their location would not be an easy problem. It is simpler because Facebook would not need an actual address; it would only need to locate the correct voting district or the state (for the Electoral College in US Presidential elections). Still, there would be error of unknown magnitude, probably impossible to calculate. It would be a very interesting research project. Whether it would be more accurate than a poll is hard to say.

Ed.: Do you think social media (or maybe search data) scraping and analysis will ever successfully replace surveys?

Grant: Surveys are such versatile, general purpose tools. They can be used to elicit many kinds information on all kinds of subjects from almost any population. These are not characteristics of social media. There is no real danger that surveys will be replaced in general.

However, I can see certain specific areas where analysis of social media will be useful. Most of these are commercial areas, like consumer sentiments. If you want to know what people are saying about your product, then going to social media is a good, cheap source of information. This is especially true if you sell a mass market product that many people use and talk about; think: films, cars, fast food, breakfast cereal, etc.

These are important topics to some people, but they are a subset of things that surveys are used for. Too many things are not talked about, and some are very important. For example, there is the famous British reluctance to talk about money. Things like income, pensions, and real estate or financial assets are not likely to be common topics. If you are a government department or a researcher interested in poverty, the effect of government assistance, or the distribution of income and wealth, you have to depend on a survey.

There are a lot of other situations where surveys are indispensable. For example, if the OII wanted to know what kind of jobs OII alumni had found, it would probably have to survey them.

Ed.: Finally .. 1400 Twitter articles in .. do we actually know enough now to say anything particularly useful or concrete about it? Are we creeping towards a Twitter revelation or consensus, or is it basically 1400 articles saying “it’s all very complicated”?

Grant: Mostly researchers have accepted Twitter data at face value. Whatever people write in a tweet, it means whatever the researcher thinks it means. This is very easy and it avoids a whole collection of complex issues. All the hard work of understanding how meaning is constructed in Twitter and how it can be measured is yet to be done. We are a long way from understanding Twitter.

Read the full article: Blank, G. (2016) The Digital Divide Among Twitter Users and Its Implications for Social Research. Social Science Computer Review. DOI: 10.1177/0894439316671698

Grant Blank was talking to blog editor David Sutcliffe.

In the first attempt to estimate Internet use at any small-scale level, they combine data from a sample survey, the 2013 Oxford Internet Survey (OxIS), with the 2011 UK census, employing small area estimation to estimate Internet use in small geographies in Britain. (Read the paper for more on this method, and discussion of why there has been little work on the geography of digital inequality.)

There are two major reasons to suspect that geographic differences in Internet use may be important: apparent regional differences and the urban-rural divide. The authors do indeed find a regional difference: the area with least Internet use is in the North East, followed by central Wales; the highest is in London and the South East. But interestingly, geographic differences become non-significant after controlling for demographic variables (age, education, income etc.). That is, demographics matter more than simply where you live, in terms of the likelihood that you’re an Internet user.

Britain has one of the largest Internet economies in the developed world, and the Internet contributes an estimated 8.3 percent to Britain’s GDP. By reducing a range of geographic frictions and allowing access to new customers, markets and ideas it strongly supports domestic job and income growth. There are also personal benefits to Internet use. However, these advantages are denied to people who are not online, leading to a stream of research on the so-called digital divide.

We caught up with Grant Blank to discuss the policy implications of this marked disparity in (estimated) Internet use across Britain.

Ed.: The small-area estimation method you use combines the extreme breadth but shallowness of the national census, with the relative lack of breadth (2000 respondents) but extreme richness (550 variables) of the OxIS survey. Doing this allows you to estimate things like Internet use in fine-grained detail across all of Britain. Is this technique in standard use in government, to understand things like local demand for health services etc.? It seems pretty clever..

Grant: It is used by the government, but not extensively. It is complex and time-consuming to use well, and it requires considerable statistical skills. These have hampered its spread. It probably could be used more than it is — your example of local demand for health services is a good idea..

Ed.: You say this method works for Britain because OxIS collects information based on geographic area (rather than e.g. randomly by phone number) — so we can estimate things geographically for Britain that can’t be done for other countries in the World Internet Project (including the US, Canada, Sweden, Australia). What else will you be doing with the data, based on this happy fact?

Grant: We have used a straightforward measure of Internet use versus non-use as our dependent variable. Similar techniques could predict and map a variety of other variables. For example, we could take a more nuanced view of how people use the Internet. The patterns of mobile use versus fixed-line use may differ geographically and could be mapped. We could separate work-only users, teenagers using social media, or other subsets. Major Internet activities could be mapped, including such things as entertainment use, information gathering, commerce, and content production. In addition, the amount of use and the variety of uses could be mapped. All these are major issues and their geographic distribution has never been tracked.

Ed.: And what might you be able to do by integrating into this model another layer of geocoded (but perhaps not demographically rich or transparent) data, e.g. geolocated social media / Wikipedia activity (etc.)?

Grant: The strength of the data we have is that it is representative of the UK population. The other examples you mention, like Wikipedia activity or geolocated social media, are all done by smaller, self-selected groups of people, who are not at all representative. One possibility would be to show how and in what ways they are unrepresentative.

Ed.: If you say that Internet use actually correlates to the “usual” demographics, i.e. education, age, income — is there anything policy makers can realistically do with this information? i.e. other than hope that people go to school, never age, and get good jobs? What can policy-makers do with these findings?

Grant: The demographic characteristics are things that don’t change quickly. These results point to the limits of the government’s ability to move people online. They say that 100% of the UK population will never be online. This raises the question, what are realistic expectations for online activity? I don’t know the answer to that but it is an important question that is not easily addressed.

Ed.: You say that “The first law of the Internet is that everything is related to age”. When are we likely to have enough longitudinal data to understand whether this is simply because older people never had the chance to embed the Internet in their lives when they were younger, or whether it is indeed the case that older people inherently drop out. Will this age-effect eventually diminish or disappear?

Grant: You ask an important but unresolved question. In the language of social sciences — is the decline in Internet use with age an age-effect or a cohort-effect. An age-effect means that the Internet becomes less valuable as people age and so the decline in use with age is just a reflection of the declining value of the Internet. If this explanation is true then the age-effect will persist into the indefinite future. A cohort-effect implies that the reason older people tend to use the Internet less is that fewer of them learned to use the Internet in school or work. They will eventually be replaced by active Internet-using people and Internet use will no longer be associated with age. The decline with age will eventually disappear. We can address this question using data from the Oxford Internet Survey, but it is not a small area estimation problem.

Read the full article: Blank, G., Graham, M., and Calvino, C. 2017. Local Geographies of Digital Inequality. Social Science Computer Review. DOI: 10.1177/0894439317693332.

This work was supported by the Economic and Social Research Council [grant ES/K00283X/1]. The data have been deposited in the UK Data Archive under the name “Geography of Digital Inequality”.

Grant Blank was speaking to blog editor David Sutcliffe.

Standing on a stage in San Francisco in early 2010, Facebook founder Mark Zuckerberg, partly responding to the site’s decision to change the privacy settings of its 350 million users, announced that as Internet users had become more comfortable sharing information online, privacy was no longer a “social norm”. Of course, he had an obvious commercial interest in relaxing norms surrounding online privacy, but this attitude has nevertheless been widely echoed in the popular media. Young people are supposed to be sharing their private lives online — and providing huge amounts of data for commercial and government entities — because they don’t fully understand the implications of the public nature of the Internet.

There has actually been little systematic research on the privacy behaviour of different age groups in online settings. But there is certainly evidence of a growing (general) concern about online privacy (Marwick et al., 2010), with a 2013 Pew study finding that 50 percent of Internet users were worried about the information available about them online, up from 30 percent in 2009. Following the recent revelations about the NSA’s surveillance activities, a Washington Post-ABC poll reported 40 percent of its U.S. respondents as saying that it was more important to protect citizens’ privacy even if it limited the ability of the government to investigate terrorist threats. But what of young people, specifically? Do they really care less about their online privacy than older users?

Privacy concerns an individual’s ability to control what personal information about them is disclosed, to whom, when, and under what circumstances. We present different versions of ourselves to different audiences, and the expectations and norms of the particular audience (or context) will determine what personal information is presented or kept hidden. This highlights a fundamental problem with privacy in some SNSs: that of ‘context collapse’ (Marwick and boyd 2011). This describes what happens when audiences that are normally kept separate offline (such as employers and family) collapse into a single online context: such a single Facebook account or Twitter channel. This could lead to problems when actions that are appropriate in one context are seen by members of another audience; consider for example, the US high school teacher who was forced to resign after a parent complained about a Facebook photo of her holding a glass of wine while on holiday in Europe.

SNSs are particularly useful for investigating how people handle privacy. Their tendency to collapse the “circles of social life” may prompt users to reflect more about their online privacy (particularly if they have been primed by media coverage of people losing their jobs, going to prison, etc. as a result of injudicious postings). However, despite SNS being an incredibly useful source of information about online behaviour practices, few articles in the large body of literature on online privacy draw on systematically collected data, and the results published so far are probably best described as conflicting (see the literature review in the full paper). Furthermore, they often use convenience samples of college students, meaning they are unable to adequately address either age effects, or potentially related variables such as education and income. These ambiguities certainly provide fertile ground for additional research; particularly research based on empirical data.

The OII’s own Oxford Internet Surveys (OxIS) collect data on British Internet users and non-users through nationally representative random samples of more than 2,000 individuals aged 14 and older, surveyed face-to-face. One of the (many) things we are interested in is online privacy behaviour, which we measure by asking respondents who have an SNS profile: “Thinking about all the social network sites you use, … on average how often do you check or change your privacy settings?” In addition to the demographic factors we collect about respondents (age, sex, location, education, income etc.), we can construct various non-demographic measures that might have a bearing on this question, such as: comfort revealing personal data; bad experiences online; concern with negative experiences; number of SNSs used; and self-reported ability using the Internet.

So are young people completely unconcerned about their privacy online, gaily granting access to everything to everyone? Well, in a word, no. We actually find a clear inverse relationship: almost 95% of 14-17-year-olds have checked or changed their SNS privacy settings, with the percentage steadily dropping to 32.5% of respondents aged 65 and over. The strength of this effect is remarkable: between the oldest and youngest the difference is over 62 percentage points, and we find little difference in the pattern between the 2013 and 2011 surveys. This immediately suggests that the common assumption that young people don’t care about — and won’t act on — privacy concerns is probably wrong.

Comparing our own data with recent nationally representative surveys from Australia (OAIC 2013) and the US (Pew 2013) we see an amazing similarity: young people are more, not less, likely to have taken action to protect the privacy of their personal information on social networking sites than older people. We find that this age effect remains significant even after controlling for other demographic variables (such as education). And none of the five non-demographic variables changes the age effect either (see the paper for the full data, analysis and modelling). The age effect appears to be real.

So in short, and contrary to the prevailing discourse, we do not find young people to be apathetic when it comes to online privacy. Barnes (2006) outlined the original ‘privacy paradox’ by arguing that “adults are concerned about invasion of privacy, while teens freely give up personal information (…) because often teens are not aware of the public nature of the Internet.” This may once have been true, but it is certainly not the case today.

Existing theories are unable to explain why young people are more likely to act to protect privacy, but maybe the answer lies in the broad, fundamental characteristics of social life. It is social structure that creates context: people know each other based around shared life stages, experiences and purposes. Every person is the center of many social circles, and different circles have different norms for what is acceptable behavior, and thus for what is made public or kept private. If we think of privacy as a sort of meta-norm that arises between groups rather than within groups, it provides a way to smooth out some of the inevitable conflicts of the varied contexts of modern social life.

This might help explain why young people are particularly concerned about their online privacy. At a time when they’re leaving their families and establishing their own identities, they will often be doing activities in one circle (e.g. friends) that they do not want known in other circles (e.g. potential employers or parents). As an individual enters the work force, starts to pay taxes, and develops friendships and relationships farther from the home, the number of social circles increases, increasing the potential for conflicting privacy norms. Of course, while privacy may still be a strong social norm, it may not be in the interest of the SNS provider to cater for its differentiated nature.

The real paradox is that these sites have become so embedded in the social lives of users that to maintain their social lives they must disclose information on them despite the fact that there is a significant privacy risk in disclosing this information; and often inadequate controls to help users to meet their diverse and complex privacy needs.

Read the full paper: Blank, G., Bolsover, G., and Dubois, E. (2014) A New Privacy Paradox: Young people and privacy on social network sites. Prepared for the Annual Meeting of the American Sociological Association, 16-19 August 2014, San Francisco, California.

References

Barnes, S. B. (2006). A privacy paradox: Social networking in the United States. First Monday,11(9).

Marwick, A. E., Murgia-Diaz, D., & Palfrey, J. G. (2010). Youth, Privacy and Reputation (Literature Review). SSRN Scholarly Paper No. ID 1588163. Rochester, NY: Social Science Research Network.

Marwick, A. E., & boyd, D. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133. doi:10.1177/1461444810365313

Grant Blank is a Survey Research Fellow at the OII. He is a sociologist who studies the social and cultural impact of the Internet and other new communication media.

A problem with national sample surveys (the usual source of data about Internet use and non-use), is that the sample sizes become too small to allow accurate generalization at smaller, sub-national areas. No one knows, for example, the proportion of Internet users in Glasgow, because national surveys simply won’t have enough respondents to make reliable city-level estimates. We know that Internet use is not evenly distributed at the regional level; Ofcom reports on broadband speeds and penetration at the county level (Ofcom 2011), and we know that London and the southeast are the most wired part of the country (Dean et al. 2012). But given the importance of the Internet, the lack of knowledge about local patterns of access and use in Britain is surprising. This is a problem because without detailed information about small areas we can’t identify where would benefit most from policy intervention to encourage Internet use and improve access.

We have begun to address this lack of information by combining two important but separate datasets — the 2011 national census, and the 2013 OxIS surveys — using the technique of small area estimation. By definition, census data are available for very small areas, and because it reaches (basically) everyone, there will be no sampling issues. Unfortunately, it is extremely expensive to collect this data, so it doesn’t collect many variables (it has no data on Internet use, for example). The second dataset, the OII’s Oxford Internet Survey (OxIS), is a very rich dataset of all kinds of Internet activity, measured with a random sample of more than 2,000 individuals across Britain. Because OxIS is unable to survey everyone in Britain, it is based on a random sample of people living in geographical ‘Output Areas’ (OAs). These areas (generally of 40-250 households) represent the fundamental building block of the national census, being the smallest geographical area for which it reports data.

Because OxIS and the census (happily) use the same OAs, we can combine national-level data on Internet use (from OxIS) with local-level demographic information (from the census) to map estimated Internet use across Britain for the first time. We can do this because we can estimate from OxIS the likelihood of an individual using the Internet just from basic demographic data (age, income, education etc.). And because the census records these demographics for everyone in each OA, we can go on to estimate the likely proportion of Internet users in each of these areas. By combining the richness of OxIS survey data with the comprehensive small area coverage of the census we can use the strengths of one to offset the gaps in the other.

Of course, this procedure assumes that people in small areas will generally match national patterns of Internet use; ie that those who are better educated, employed, and young, are more likely to use the Internet. We assume that this pattern isn’t affected by cultural or social factors (e.g. ‘Northerners just like the Internet more’), or by anything unusual about a particular group of households that makes it buck national trends (eg ‘the young people of Wytham Street, Oxford just prefer not to use the Internet’).

So what do we see when we combine the two datasets? What are the local-level patterns of Internet use across Britain? We can see from the figure that the highest estimated Internet use (88-89%) is concentrated in the south east, with London dominating. Bristol, Southampton, and Nottingham also have high levels of use, as well as the rest of the south (interestingly, including rural Cornwall) with estimated usage levels of 78-83%. Leeds, York and Manchester are also in this category. In the lowest category (59-70% use) we find the entire North East region. Cities show much the same pattern, with southern cities having the highest estimated Internet use, and Newcastle and Middlesbrough having the lowest.

There isn’t room in this post to explore and discuss all the patterns (or to speculate on the underlying reasons), but there are clear policy implications from this work. The Internet has made an enormous difference in our social life, culture, and economy; this is why it is important to bring people online, to encourage them all to participate and benefit. However, despite the importance of the Internet in Britain today, we still know very little about who is, and isn’t connected. We hope this approach (and this data) can help pinpoint the areas of greatest need. For example, the North East is striking — even the cities don’t seem to stand out from the surrounding rural areas. Allocating resources to improve use in the North East would probably be valuable, with rural areas as a secondary priority. Interestingly, Cornwall (despite being very rural) is actually above average in terms of likely Internet users, and is also the recipient of a major European Regional Development Fund effort to extend their broadband.

Actually getting access via fibre-optic cable is just one part of the story of Internet use (and one we don’t cover in this post); but this is the first time we have been estimate the likely use at a local level, based on the known characteristics of the people who live there. Using these small area estimation techniques opens a whole new area for social media research and policy-making around local patterns of digital participation. Going forward, we intend to expand the model to include urban-rural differences, the index of multiple deprivation, occupation, and socio-economic status. But there’s already much more we can do with these data.

References

Dean, D., DiGrande, S., Field, D., Lundmark, A., O’Day, J., Pineda, J., Zwillenberg, P. (2012) The connected world: The Internet economy in the G-20. Boston: Boston Consulting Group.

Malecki, E.J. & Moriset, B. (2008) The digital economy: Business organization, production processes and regional developments. London: Routledge.

Ofcom (2011) Communications infrastructure report: Fixed broadband data. [accessed on 23/9/2013 from http://stakeholders.ofcom.org.uk/binaries/research/broadband-research/Fixed_Broadband_June_2011.pdf ]

Read the full paper: Blank, G., Graham, M., and Calvino, C. (2014) Mapping the Local Geographies of Digital Inequality. [contact the authors for the paper and citation details]

Grant Blank is a Survey Research Fellow at the OII. He is a sociologist who studies the social and cultural impact of the Internet and other new communication media. He is principal investigator on the OII’s Geography of Digital Inequality project, which combines OxIS and census data to produce the first detailed geographic estimates of Internet use across the UK.