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Study reveals how Twitter shapes public opinion

'Once public opinion stabilises, it's difficult to change' 

12 March 2014

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By Manju Manglani, Editor (@ManjuManglani)

Dominant, majority viewpoints emerge quickly on Twitter and, once stabilised, are difficult to change, a new study has found.

The findings can be used to shape how social media campaigns are run and influence the way businesses market their products and services.

"Companies can analyse the characteristics and behaviour patterns of their supporters and protesters to explore whether the measures they take can influence public opinion and which opinion may succeed," said Fei Xiong, a lecturer at Beijing Jiaotong University, who gathered and analysed the data with professor Yun Liu.

Xiong and Liu discovered that public opinion on Twitter often evolves rapidly and levels off quickly into an ordered state in which one opinion remains dominant. In true social media form, this consensus is often driven by the endorsements of increasingly larger groups, which tend to have the most influence.

The study also found that, when dominant opinions emerge, they tend not to achieve complete consensus. In fact, Xiong said, when Twitter users who hold minority views are faced with overwhelming opposition, they are still not likely to change their opinions.

Since public opinion levels off and evolves into an ordered state within a short time, small advantages of one opinion in the early stages can turn into a bigger advantage during the evolution of public opinion, Xiong said.

"Once public opinion stabilises, it's difficult to change," he added.

The work also revealed that Twitter users are more likely to work to change the opinions of others than to admit to changes of their own.

Xiong envisions that large organisations may benefit from applying this work toward developing "network applications" that would move beyond simply collecting and analysing users' opinions and allow them to develop and test hypotheses about what really works.

The full research findings are published in Chaos: An Interdisciplinary Journal of Nonlinear Science.

 

 

 

 

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