Who uses New Media?

What are the patterns of new-media usage in the UK by age, social class, gender. Is there still a digital divide?

In 2019, almost nine in ten (87%) UK households had internet access, and adults who use the internet spent, on average, 3 hours 15 minutes a day online (in September 2018) (1)

Around 70% of UK adults have a social media account and about one in every five minutes spent online is on social media (1)

The number of households connected to the internet and the use of New Media has increased rapidly in the last decade, but statistics from OFCOM clearly show that there are still differences in new media usage by age, social class and gender.

For an overview of what the New Media are, please see these two posts:

The generation divide

New media usage varies significantly by age.

This is especially clear if we contrast the youngest age groups (as classified by OFCOM) of 16-24 year olds with the oldest of 74+

The differences are less marked, but still clear if we look at a wider variety of age groups. I’ve deliberately selected two consecutive age groups below (45-54 and 55-64) because there appears to be quite a significant drop off in new media usage between these two age categories.

AGE 16-24s 45-54s: 55-64s:

 

AGE 75+
·         99% use a mobile phone

·         79% watch on-demand or streamed content

·         93% have a social media profile

·         1% do not use the internet (2)

·         47% play games online (4)

 

·         98% use a mobile phone

·         69% watch on-demand or streamed content

·         76% have a social media profile

·         7% do not use the internet (2)

·       10% play online games (4)

·         96% use a mobile phone

·         43% watch on-demand or streamed content

·         58% have a social media profile

·         19% do not use the internet (2)

·         5% play online games (4)

 

·         81% use a mobile phone

·         22% watch on-demand or streamed content

·         20% have a social media profile

·         48% do not use the internet (2)

·         5% play games online (4)

 

The social class digital divide

Working-age adults in DE socio-economic group1 households are more than three times as likely as those in non-DE households to be non-users of the internet (14% vs. 4%). (1)

The contrast is best shown by comparing the highest socio-economic group (AB) with the lowest socio-economic group (DE):

Socio-Economic Group AB:

  • 97% use a mobile phone
  • 73% watch on-demand or streamed content
  • 74% have a social media profile
  • 57% correctly identify advertising on Google
  • 6% do not use the internet (2)

Socioeconomic Group DE:

  • 93% use a mobile phone
  • 46% watch on-demand or streamed content
  • 56% have a social media profile
  • 37% correctly identify advertising on Google
  • 23% do not use the internet (2)

The digital gender divide

  • In 2017, women (81%) continue to be more likely to have a profile/ account, compared to men (74%). (4)
  • Women are more likely than men to say they have ever seen content that upset or offended them in social media over the past year (58% vs. 51%). (4)
  • (50%) of men say they are ‘very’ interested in the news (50%) compared to only a third (34%) of women. Twice as many women (15%) as men (8%) are not interested. (4)
  • A quarter of men (24%) play games online, compared to 9% of women. (4)

Conclusions – is there a significant new media digital divide in the UK in 2019?

  • While there does seem to be a very significant generation divide between the very youngest and oldest, the differences between young adults and those in their early 50s is relatively small.
  • There does appear to be some evidence that those in class DE are less well connected than those in class DE with nearly a quarter of adults in class DE not being connected to the internet.
  • There also appear to be quite significant differences by gender: women are more likely to have social media profiles while men are much more likely to take an interest in the news.

Sources

  1. OFCOM – Online Nation 2019 – https://www.ofcom.org.uk/__data/assets/pdf_file/0024/149253/online-nation-summary.pdf
  2. OFCOM – Media Use and Attitudes Report 2019 – https://www.ofcom.org.uk/__data/assets/pdf_file/0021/149124/adults-media-use-and-attitudes-report.pdf
  3. OFCOM’s Interactive data link.
  4. https://www.ofcom.org.uk/__data/assets/pdf_file/0011/113222/Adults-Media-Use-and-Attitudes-Report-2018.pdf

 

 

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Applying material from the item, analyse two reasons why younger people are generally less religious than older people

This is one possible example of a 10 mark ‘with item’ question which could come up in the AQA’s A level sociology paper 2: topics in sociology (section B: beliefs in society option). 

Read the item, and then answer the question below.

Item

Older people are more likely to both attend church and express religious beliefs than younger people.

Some sociologists have suggested that this is due to changes which occur during the life-course. Other sociologists believe this trend is more about social changes resulting in generational differences.

Applying material from the item, analyse two reasons why younger people are generally less religious than older people

The first reason why older people are more religious is that as they come to the end of their ‘life course’, they are simply biologically closer to death which means they start to think more about what happens after death. This is something which all religions deal with, and so it could simply be that older people become more religious because they find a suitable explanation to their questions about the afterlife in religion.

This could be especially the case today, as modern society is obsessed with ‘youth and life’ and so religion is one of the few places people close to death might find solace.

A related life course related factor is social isolation. As people enter retirement, they lose their work place connections, and are more likely to see their friends die. Attending church could be a way of making up for these lost connections.

The second possible reason is social changes – meaning that each successive generation is less religious than the previous generation.

The church has gradually become disengaged from society and so has less influence over social life: thus children today are much less likely to see religious authority being exercised in politics, and religion has also lost its influence in education: RE is now somewhat watered down compared to what it used to be: presenting religion as a choice rather than a necessity.

Also, now that society has become more postmodern, it emphasizes, fun, diversity and choice, all of which traditional religion at least doesn’t offer as much of: people would rather spend Sunday relaxing rather than in church, and this is very much normal today.

As a result of all the above, parents are much less likely to socialize their children into religious beliefs and practices, which explains the decline in religion across the generations and between younger and older people today.

Why are older people more religious than younger people?

Older people are more religious than younger people, as measure by religious participation and religions belief. This post explores three reasons why this might be.

The biological affect of ageing

It seem ‘natural’ to assume that as people get older and closer to death, they become more interested in what happens after they die, which is something religions have answers to. It may be that people become more religious closer to death because they find the thought of an afterlife more comforting than the thought of themselves just turning to dust.

This kind of fits in with the postmodern view that people actively use religion people use religion to help them die, rather than to help them live.

Older people are more detached from society

Older people tend to be more socially isolated than younger or middle aged people. The older you get, the more likely you are to have witnessed your friends dying and you are more likely to have serious health issues which prevent you from interacting with friends and family.

This is especially the case with women who live longer than men, and thus are more likely to outlive their male partners. This could go some way to explaining the higher levels of religiosity among women compared to men.

Social changes mean each generation is less religious than the previous generation 

In this theory, it is not so much that the religious beliefs of individuals change as they get older, rather that social changes mean that each generation is less religious than the previous generation.

Secularization has resulted in religion becoming disengaged from society, so it is much less part of day to day social life: religion doesn’t influence politics like it used to, the status of religious education in schools has declined, and church attendance has dropped.

Each successive generation is also less likely to socialize their children into religious beliefs and practices, thus resulting in a gradual decline in religiosity generation after generation.

 

Religion and Age

This post presents an examination of the relationship between religious belief, religious participation and age.

Younger people tend to be less religious than older  people

  • Recent (2018) research by PEW compared the religious beliefs and practices of 18-39 year olds with those aged 40 and over. They found that younger people are less religious than old people in 41 countries, but there are only 2 countries in which younger people are more religious. There is no difference in 60 countries.

  • According to the 2011 UK census, young people are much more likely to report that they have no religion
    • People aged under 25 made up 31% of the population as a whole, but 39% of those reporting they had no religion
    • Those aged 65+ made up 16.5% of the population as a whole, but just 5.6% of those reporting they had no religion.
  • Also according to the UK National Census, ethnic minority religions tend to have a much younger age profile than Christianity or No religion. For example, 85% of Muslims are aged under 50, compared to around 55% of Christians.

Age and participation in New Religious Movements and the New Age Movement

  • Eileen Barker’s research into The Moonies (a world rejecting sect) found that the membership base was relatively young, with most members being aged between 18-30.
  • The New Age Movement tends to be made up of middle aged people, especially those in their late 30s and 40s.

 

https://www.theguardian.com/news/datablog/2013/may/16/uk-census-religion-age-ethnicity-country-of-birth

 

 

Twitter Users by Occupation and Social Class

The middle classes and especially those in creative industries are more likely to be on twitter, but finding this out is more difficult than you might think, at least according to some recent research:

Who Tweets?: Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data

This post is a brief summary of the methods and findings of the above.

Introduction/ Context/ Big Data

90% of the world’s data has been generated in the past 2 years and the trend is apparently exponential, the key challenges of harnessing this data (known as the 5Vs: volume,veracity, velocity, variety and value) are not so easily overcome.

The primary criticism of such data is that it is there to be collected and analysed before the question is asked and, because of this, the data required to answer the research question may not be available with important information such as demographic characteristics being absent.

The sheer volume of data and its constant, flowing, locomotive nature provides an opportunity to take the ‘pulse of the world’ every second of the day rather than relying on punctiform and time-consuming terrestrial methods such as surveys. Only 1% of Twitter users in the UK amounts to around 150,000 users. Even a tiny kernel of ‘useful’ data can still amount to a sample bigger than some of the UK’s largest sample surveys

However, social media data sources are often considered to be ‘data-light’ as there is a paucity of demographic information on individual content producers.

Yet, as Savage and Burrows argue, sociology needs to respond to the emergence of these new data sources and investigate the ways in which they inform us of the social world. One response to this has been the development of using ‘signatures’ in social media as proxies for real world events and individual characteristics

This paper builds on this work conducted at the Collaborative Online Social Media Observatory (COSMOS),through proposing methods and processes for estimating two demographic variables: age and occupation (with associated class).

How Do Twitter Users Vary by Occupation and Social Class – Methods

The researchers used a sample 32, 032 twitter profiles collected by COSMOS, relying on the entry in the ‘profile’ box to uncover occupation and class background.

They took the occupation with the most number of words as the primary occupation, and, if multiple occupations are listed, they took the first occupation as the primary occupation.

They then randomly selected 1,000 cases out of the 32,032 to which an occupation was assigned and three expert coders visually inspected the results of 1000 twitter profiles in anticipation of inaccuracies and errors.

They found that 241 (so 24%) had been misclassified, with a high level of inter-rater reliability.

The main problems of identification stemmed from the multiple meanings of many words related to occupations, Hobbies, and with obscure occupations. For example, people might refer to themselves as a ‘Doctor Who fan’ or a ‘Dancer trapped in a software engineer’s body’.

So what is the class background of twitter users?

The table below shows you three different data sets – the class backgrounds as automatically derived from the entire COSMOS sample of profiles, the class background of the 32 000 sample the researcher used and the class backgrounds of the 1000 that were visually verified by the three expert coders (for comments on the differences see ‘validity problems’ below).

journal.pone.0115545.g001

There is a clear over representation of NS-SEC 2 occupations in the data compared with the general UK population which may be explained by the confusion between occupations and hobbies and/or the use of Twitter to promote oneself or one’s work. NS-SEC 2 is where occupations such as ‘artist’, ‘singer’, ‘coach’, ‘dancer’ and ‘actor’ are located and the utility of the tool for identifying occupation for this group is further exacerbated by the fact that this is by far the most populous group for Twitter users and the largest group in the general UK population by 10% points. Alternatively, if the occupation of these individuals has been correctly classified then we can observe that they are over represented on Twitter by a factor of two when using Census data as a baseline measure.

Occupations such as ‘teacher’, ‘manager’ and ‘councillor’ are not likely to be hobbies but there is an unusually high representation of creative occupations which could also be pursued as leisure interests with 4% of people in the dataset claiming to be an ‘actor’, 3.5% an ‘artist’ and 3.5% a ‘writer’. An alternative explanation is that Twitter is used by people who work in the creative industries as a promotional tool.

Validity problems with the social-class demographics of twitter data

Interestingly, the researchers rejected the idea that people would just outright lie about their occupations noting that ‘previous research [has] indicated that identity-play and the adoption of alternative personas was often short-lived, with ‘real’ users’ identities becoming dominant in prolonged interactions. The exponential uptake of the Internet,beyond this particular group of early adopters,was accompanied with a shift in the presentation of self online resulting in a reduction in online identity-play’.

The COSMOS engine does automatically identify occupation, but it identifies occupation inaccurately – and the degree of inaccuracy varies with social class background. The researchers note:

‘unmodified occupation identification tool appears to be effective and accurate for NS-SEC groups in which occupational titles are unambiguous such as professions and skilled trades (NS-SEC 1,3,4 and 5). Where job titles are less clear or are synonymous with alternative activities (NS-SEC 2, 6 and7) the requirement for human validation becomes apparent as the context of the occupational term must betaken into account such as the difference between “I’m a dancer in a ballet company”and “I’m a dancer trapped in the body of a software engineer’.

The researchers note that the next step is to further validate their methodology through establishing the ground-truth via ascertaining the occupation of tweeters through alternative means, such as social surveys (an on-going programme of work for the authors).

Comments

In some ways the findings are not surprising – that the middle class professionals and self-employed are over-represented on twitter, but if we are honest, we don’t know by how much, because of the factors mentioned above. It seems fairly likely that many of the people self-identifying on twitter as ‘actors’ and so on don’t do this as their main job, but we just can’t access this method by twitter alone.

Thus this research is a reminder that hyper reality is not more real than actual reality. In hyper-reality these people are actors, in actual reality, they are frustrated actors. This is an important distinction, and this alone could go some way to explaining why virtual worlds can be so much meaner than real-worlds.

This research also serves as a refreshing reminder of how traditional ‘terrestrial’ methods such as surveys are still required to ascertain the truth of the occupations and social class backgrounds of twitter users. As it stands if we left it to algorithms we’d end up with 25% of people bring incorrectly identified, which is a huge margin of error. If we leave these questions up to twitter, then we are left with a very misleading picture of ‘who tweets’ by social class background.

Having said this, it is quite possible for further rules to be developed and applied to algorithms which could increase the accuracy of automatic demographic data-mining.