Periodic Reporting for period 3 - NEWSFLOWS (Modeling news flows: How feedback loops influence citizens' beliefs and shape societies)
Période du rapport: 2024-01-01 au 2024-01-31
Our beliefs about society are largely based on information that we encounter through media. This happens more and more in an "unbundled” form: Single news items are distributed through sharing on social media, sorted by algorithms, and encountered on platforms on which they were not originally published. Many argue that this leads to so-called “echo chambers” and “filter bubbles”, communities of people that are only exposed to information they agree with. This is thought to lead to increasing polarization of society, and to a lack of diversity in people’s (virtual) communities. But a growing body of evidence suggests that these metaphors are misleading. In fact, as recent discussions on so-called “fake news” illustrate, biased and/or extreme information is not locked up in filter bubbles or echo chambers, but spreads from niche communities into mainstream media and politics.
NEWSFLOWS develops an alternative model of how information spreads in today’s media ecosystem – a model based on so-called feedback loops, which are essential for the modern complex system of information flows. To give an example of a feedback loop: If a news item receives many shares on social media, this may let a recommendation algorithm show it to even more users (and journalists and politicans), making it more likely that they will act on it, again increasing the number of shares, etc. Crucially, neither the algorithm, nor the users, nor the writers alone determine the eventual spread, but a combination of their influences and feedback loops. Theoretical models and empirical methods to study such feedback loops in the social sciences and humanities are scarce. NEWSFLOWS extends innovative methods as online field experiments, data donations, and automated content analysis to conduct such studies. This will greatly enhance the theoretical understanding of news flows, but also enable media organizations to develop products conforming to calls for "responsible AI".
NEWSFLOWS develops an alternative model of how information spreads in today’s media ecosystem – a model based on so-called feedback loops, which are essential for the modern complex system of information flows. To give an example of a feedback loop: If a news item receives many shares on social media, this may let a recommendation algorithm show it to even more users (and journalists and politicans), making it more likely that they will act on it, again increasing the number of shares, etc. Crucially, neither the algorithm, nor the users, nor the writers alone determine the eventual spread, but a combination of their influences and feedback loops. Theoretical models and empirical methods to study such feedback loops in the social sciences and humanities are scarce. NEWSFLOWS extends innovative methods as online field experiments, data donations, and automated content analysis to conduct such studies. This will greatly enhance the theoretical understanding of news flows, but also enable media organizations to develop products conforming to calls for "responsible AI".
To achieve these objectives, the project team worked on multiple areas.
(1) Theory development
We conducted a thorough literature review and developed a new theoretical framework for studying feedback loops in news flows. This has resulted in a working paper that has been presented at multiple occasions and has been submitted to a journal.
(2) Methods and tool development
We developed and improved computational methods to (a) trace information flows on social media and beyond, (b) conduct data donation studies and analyze the traces people leave online, and (c) to conduct experiments for understanding how people interact with news recommender systems.
(3) Substantive insights
Based on our analyses, we gained insights into how news disseminates on Telegram and also how news disseminates cross-platform. We learned where on YouTube people encounter news-related content. Also, we learned about over-time developments in browsing patterns, and how these can be explained by feedback loops.
(1) Theory development
We conducted a thorough literature review and developed a new theoretical framework for studying feedback loops in news flows. This has resulted in a working paper that has been presented at multiple occasions and has been submitted to a journal.
(2) Methods and tool development
We developed and improved computational methods to (a) trace information flows on social media and beyond, (b) conduct data donation studies and analyze the traces people leave online, and (c) to conduct experiments for understanding how people interact with news recommender systems.
(3) Substantive insights
Based on our analyses, we gained insights into how news disseminates on Telegram and also how news disseminates cross-platform. We learned where on YouTube people encounter news-related content. Also, we learned about over-time developments in browsing patterns, and how these can be explained by feedback loops.
Already now, the approaches outlined above go beyond the state-of-the art of how similar questions are studied. By the end of the project, next to the achievements outlined above, have achieved the following:
(1) Consolidated the methods and tools we developed
Already now, the methods and tools we developed go beyond what is common in the field. They are accessible open-source, but we will continue to improve them and to make new ones available.
(2) More empirical insights
In particular, next to the studies we already conducted or are in the process of conducting, we will conduct a large international study, which puts our theoretical and empirical insights into test.
(3) A theory of feedback loops
While a first paper proposing a theoretical conceptualization of feedback loops in news flows, we will - by the end of the project - have an empirically validated and enhanced/updated model. The explicit aim is to go beyond the metaphores commonly used nowadays (echo chamber, filter bubble).
(1) Consolidated the methods and tools we developed
Already now, the methods and tools we developed go beyond what is common in the field. They are accessible open-source, but we will continue to improve them and to make new ones available.
(2) More empirical insights
In particular, next to the studies we already conducted or are in the process of conducting, we will conduct a large international study, which puts our theoretical and empirical insights into test.
(3) A theory of feedback loops
While a first paper proposing a theoretical conceptualization of feedback loops in news flows, we will - by the end of the project - have an empirically validated and enhanced/updated model. The explicit aim is to go beyond the metaphores commonly used nowadays (echo chamber, filter bubble).