Interview with Dr. Michael Wojatzki
Michael Wojatzki completed his doctorate in January 2019 at the graduate school "User-Centred Social Media" at the University of Duisburg-Essen. Besides his academic career, Michael has worked for several companies in the information industry and software development. His research focuses on automatic, AI-based systems that recognise and analyse opinions shared in social media.
- Data science
- Artificial intelligence (machine learning)
- Opinion mining / public opinion research
Of interest to
- Data scientists
- Computer scientists
- Social media manager
What does „stance“ mean in the context of social media?
Stance refers to an evaluation, either positive or negative, which is directed towards a person, thing or idea. People’s stance drives them to vote for a certain party or candidate, to buy a certain product, or to avoid or approach people. As people today express their stance in large quantities on social media sites, social media can be an important source for assessing the stance of groups or society as a whole.
You analysed if it is possible to predict how people will position themselves to controversial topics, for instance climate change or gender equality. What are your results?
It is actually possible to make such predictions. However, two prerequisites must be given: First, the system undertaking the analysis needs training data to understand a certain topic. Such data can be obtained, for example, from whether people liked relevant Facebook posts. Second, prediction models are always topic-specific. If you trained such a model to predict positions on climate change, it cannot readily be used to predict positions on gender equality. Even under these conditions, predicting the position of individuals is very challenging. The difficulty for computers here is that individuals often have conflicting positions—e.g. find it immoral to eat meat but still do it. Predictions at a group level can be made with high accuracy. In such a case, we would try to figure out what percentage of a group agrees or disagrees with a position.
How can stance analysis in social media affect society positively or negatively?
In machines that are able to automatically understand stance on a large scale, there is great potential for but also a danger to society. For example, if people start to use stance detection to only engage with content that aligns with their own stance, existing echo-chambers will be amplified and discourses on controversial issues—that incorporate every member of society—will not take place. Ultimately this may lead to a more fractured society which is unable to find consensus. In addition, stance detection also provides the technical basis for an all-pervading censorship or the persecution of political dissidents. At the same time, automatic stance detection can improve the efficiency with which social media users or organisations discover, group or filter social media posts that express stance towards targets they are interested in. In this way, automatic stance detection can help the society as a whole to communicate more efficiently, and thus to make better decisions.
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