Professor Mario Haim combines communication science with information technology

7 Dec 2022

Newly appointed professor at LMU, Mario Haim researches computational communication – and sees something of a paradigm shift in his discipline.

Algorithms and big data are influencing our modern communication – and raising many new questions for communication science: How does constant smartphone use affect human wellbeing? What role do huge platforms play in the world of news? And what are suitable methods for researching such questions? Professor Mario Haim, a new member of staff at LMU, studies such topics – and combines traditional communication science with IT methods.

Since February of this year, Mario Haim has held the newly established Chair of Communication Science with a special focus on Computational Communication Research at LMU. Originally from Austria, he studied business IT and social science in Augsburg and Helsinki as well as at LMU. He obtained his doctorate at LMU in 2018 with the dissertation Orientation of Online Journalism Toward Its Audiences. In Norway, he pursued postdoctoral research at the University of Stavanger and was a research fellow at Oslo Metropolitan University. He was a visiting scholar at the Columbia Journalism School in New York and the University of Southern Denmark in Odense. Haim held a junior professorship in data journalism at Leipzig University from 2019 to February 2022, when he took up an offer to join LMU’s Department of Media and Communication.

One topic that Haim researches at LMU is how algorithms influence public communication. In his most recent publications, he investigated the “platformization” of news, the variety of Google hits, and questions such as how search engines can help prevent suicides. He has also published papers on stereotypes and sexism in user comments about journalists and people’s susceptibility to fake news in social media depending on their political orientation.

Professor Mario Haim

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Social effects like we witnessed 40 years ago

Haim explains how computational communication science is still based on the theories of classical communication science. “One example is the phenomenon we witnessed during the pandemic lockdowns: When people connect intensively with like-minded souls in the increasingly closed communication spaces of social media, where they do not hear any contrary opinions and only experience the likes of their circle of friends, they can feel that their opinion is widely held – irrespective of whether this is actually true,” says Haim. “This brings us to the spiral of silence theory, for instance, a classical theory of communication science. It’s a social effect that was already prevalent 40 years ago – only that today it’s being amplified by algorithmically curated, fragmented communication.”

His second major area of study is methodological research. Like the social sciences in general, communication science has to develop and improve its methods. “We need a repertoire of methods with which we can research these new aspects of our subject in the first place.” Pure surveys do not work anymore when researching digital communication behavior. “We know from studies that people totally misjudge themselves in this regard. They underestimate how much time they spend on their smartphones, for example, and overestimate their news consumption.”

Another problem: “How do we collect the data from four million Facebook feeds? And what do we then do with it?” Instead of surveys, observations, and content analyses, researchers today have to grapple with automated observation. This includes methods such as tracking, computer-aided text and image analysis, and “agent-based modeling,” a special, individual-based type of computer-aided simulation. “To evaluate the data, researchers need AI methods. With tweets, posts, and YouTube videos, they not only have much more data than before, but data of a very different kind – coupled with attributes such as likes, which indicate the popularity of a post.” This also calls for the “informational methods” of natural language processing, machine learning, and machine vision.

Paradigm shift in communication and social sciences

“But how can our scientists apply such methods without needing an additional degree in computer science?” asks Haim. To explore this question, he studies automated content analysis in journalism research and the recording of Facebook data – and not least, he develops research software for communication science.

“We’re experiencing a paradigm shift in the methods of communication and social sciences,” says Haim. “The question of what a representative, valid Twitter survey looks like, for example, is not at all easy to answer. First of all, we need to translate the clear-cut requirements that we possess for methods such as surveys so that they become suitable for public digital communication.” Among other things, this involves reliable standards and ethical norms.

Because media companies like Facebook, TikTok, and Telegram are often reluctant to give scientists access to anonymized data, researchers sometimes resort to data donations. “Under EU law, every European citizen can request their data from companies like Instagram. In surveys, we ask people to do this and to send us the data in abbreviated and anonymized form for research purposes.”

This is “a very time-consuming but data compliant method.” Unfortunately, the response rate tends to be poor. “The optimal way of approaching people is currently a hot topic in the field.” In answering it, the findings of traditional survey research and of donation research can be usefully applied. Mario Haim and his team have been known to ask students for data donations outside lectures. “And colleagues from the University of Amsterdam have approached revelers standing in line at a music concert.”

In his podcast, Haim discusses current topics in his field with various guests.

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