Twitter misrepresents the real world, computer scientists wa
Source: Sarah Knapton
Social networking sites like Twitter and Facebook should not be used to gauge human behaviour or trends because they are too biased, scientists have warned.
Increasingly, social researchers and media organisations use sites to glean information about public views and interests.
But computer scientists at McGill University in Montreal and Carnegie Mellon University in Pittsburgh warn that the data omits the opinion of large portions of the population who are either under-represented, or who choose not to engage in social media.
They claim the sites ‘misrepresent the real world.’
One of the major problems with sites like Twitter, Pinterest or Facebook is ‘population bias’ where platforms are populated by a very narrow section of society.
Latest figures on Twitter suggest that just five per cent of over 65s use the platform compared with 35 per cent for those aged 18-29. Similarly far more men use the social networking site than women.
Instagram has a particular appeal to younger adults, urban dwellers, and non-whites.
In contrast, the picture-posting site Pinterest is dominated by females aged between 25 and 34. LinkedIn is especially popular among graduates and internet users in higher income households.
Although Facebook is popular across a diverse mix of demographic groups scientists warn that postings can be skewed because there is no ‘dislike’ button. There are also more women using Facebook than men, 76 per cent of female internet users use the site compared with 66 per cent of males.
“A common assumption underlying many large-scale social media-based studies of human behaviour is that a large-enough sample of users will drown our noise introduced by peculiarities of the platform’s population,” said lead author Derek Ruths, an assistant professor in McGill's School of Computer Science.
“These sampling biases are rarely corrected for, if even acknowledged.”
The researchers also claim that the way in which sites direct people to links also leads to interest bias. The design of a platforms can dictate how users behave and, therefore, limit what behaviour can be measured.
And a large number of spammers and bots, which masquerade as normal users on social media, get mistakenly incorporated into measurements and predictions of human behaviour.
In recent years, studies have claimed the ability to predict everything from summer blockbusters to fluctuations in the stock market through social media. Some researchers say it is possible to map the spread of disease.
But the computer scientists claim the flaws in big data sets for research could have ‘huge implications.’ Thousands of research papers each year are based on skewed information taken from social media, they claim.
"Many of these papers are used to inform and justify decisions and investments among the public and in industry and government," says assistant Professor Ruths.
Co-author Juergen Pfeffer of Carnegie Mellon, added: "People want to say something about what's happening in the world and social media is a quick way to tap into that. You get the behaviour of millions of people -- for free.
"Not everything that can be labelled as 'Big Data' is automatically great."
The research was published in the journal Science.
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