Projects

Societal Impact Of Unreliable Scientific Information: The Influence Of Retracted Scientific Articles On Public Perceptions, Attitudes, And Evidence-Informed Decision-Making (2024-2028)

Societal Impact Of Unreliable Scientific Information: The Influence Of Retracted Scientific Articles On Public Perceptions, Attitudes, And Evidence-Informed Decision-Making (2024-2028)

Project funded by the Academy of Finland, 2024-2028.
More information: https://research.fi/en/results/funding/81333

Research Group

Senior Research Fellow Kim Holmberg (team leader)
Senior Researcher Ashraf Maleki

Abstract

In an era marked by unprecedented information access, the rampant spread of misinformation and dissemination of unreliable scientific claims present significant societal challenges. This research aims to address concerns regarding the impact of unreliable scientific information on public trust, decision-making, and societal well-being. Focusing on online platforms, the study seeks to unravel how such information spreads, amplifies, and influences critical issues like public health and environmental policies. By examining its influence on public perceptions, attitudes, and pseudoscientific movements, our goal is a comprehensive understanding of challenges posed by unreliable scientific information. Combining qualitative and quantitative methods, this research sheds light on the complex interplay, offering insights for evidence-based strategies to mitigate adverse effects and foster a more informed public.

Wellbeing From Sense Of Belonging (2021-2024)

Wellbeing From Sense Of Belonging – Measuring And Developing Societal Impact Of Local Sports Clubs (2021-2024)

Project funded by the Ministry of Education and Culture (2021-2024).

Research Group

Researcher Juha Hedman (team leader)
Senior Research Fellow Kim Holmberg
Postdoctoral Researcher Timo Räikkönen
Senior Researcher Loretta Saikkonen

Abstract

The aim of the research is to broaden the understanding of the local mechanisms through which the dual goals of sports policy – expanding active participation in sport and improving the quality of elite sports – are realized in club activities and guide the social impact of club activities. The project will thus respond to the growing need for sports clubs to measure and demonstrate their social impact transparently and regularly. Furthermore, the project will complement the information gap on local mechanisms that 1) promote cohesion in sports club activities (grouping, social skills, interactivity, and so-called 21st millennium skills) and 2) further transform cohesiveness into well-being of individuals and communities.

Altassess – The Applicability Of Altmetrics For Research Assessment (2020-2024)

Altassess – The Applicability Of Altmetrics For Research Assessment (2020-2024)

Project funded by the Academy of Finland, 2020-2024.

Research Group

Senior Research Fellow Kim Holmberg (team leader)
Senior Researcher Ashraf Maleki
Senior Researcher Sanna Malinen
Research Assistant Jenni Virtanen

Co-operation

Assistant Professor Timothy D. Bowman
Dr. Fereshteh Didegah

Abstract

Scholarly communication is currently undergoing a dramatic change, as scholars are increasingly using social media to discover, consume, disseminate, and discuss research information. In addition to scholars,
the public can also take part in the online discussions and share the research documents they discover to their online networks. These online events around scientific outputs (e.g., articles, datasets, code), whether
generated through the actions of researchers or the public, leave digital traces that can be tracked, harvested, and analyzed. These traces, and the research field analyzing them, are called altmetrics.

The research field of altmetrics analyzes these mentions of scientific outputs from various online sources, such as various social media sites, blogs, and news sites, with the purpose of gaining new insights into how this activity might be used to enhance research assessment, support open science, and demonstrate some form of societal impact. Understanding who consumes, disseminates, or discusses scientific documents online and for what reason has a profound influence on the applicability of altmetrics for research assessment. This project will analyze an extensive set of data aggregated from different online data sources and use a mixed methods approach with a combination of advanced statistics, social network analysis, and content analysis methods to examine the data.

The results of this project will help researchers, research administrators, funders, and science policy makers to better understand why and by whom their research (or the research they have funded) is being shared and discussed in different contexts and with that, give a more comprehensive and contextualized understanding of the applicability of altmetrics for research assessment. The results of this project can, besides advance altmetrics research, inform and reform the major contexts where research is being assessed by demonstrating how altmetrics can (or cannot) complement existing assessment methods and metrics. The results of this project can also have a positive impact on the society as scholars will have a greater understanding of the online visibility and the attention their work attracts, thus incentivizing them to communicate their research findings and openly share their work to wider audiences beyond academia.

Completed projects

Memestonks – Developing An Early Signal System To Identify Shifts In Global Stock Market Trends (2021- 2023)

Memestonks – Developing An Early Signal System To Identify Shifts In Global Stock Market Trends (2021- 2023)

Funded by the Finnish Foundation for Stock Promotion, Nordea Bank Foundation, and Savings Banks Research Foundation, 2021-2022.

Research Group

Senior Research Fellow Kim Holmberg (team leader)
Systems Developer Olli Jalonen
Trainee Aki Raittila

Abstract

Advances in the ability to collect and analyze tremendous amounts of data has seen an increase in efforts to develop methodology to analyze social media conversations in attempts to forecast everything from the results of presidential elections (Liu et al., 2021) to Bitcoin price trends (Cavalli & Amoretti, 2021), and from traffic patterns (Yao & Qian, 2021) to spread of influenza (Wang et al., 2020). In attempts to predict stock market trends machine learning algorithms and neural networks have been developed to analyze and combine vast amounts of numerical data about for instance stock price history and textual data from different news sources (Zhai, Hsu, & Halgamuge, 2007; Nelson, Pereira, & de Oliveira, 2017; Oncharoen & Vateekol, 2018; Awan, 2021). By combining both technical indicators and data from media these approaches have in fact with high accuracy been able to predict whether the price of a specific stock will go up or down in the near future. Another approach in predicting stock markets is to use actual data about consumer behavior. Companies such as Robin Hood provide tools for commission-free investing in stocks and in return they get real-time information about customer behavior and data about investing patterns. However, the decision to invest in certain stocks often precedes by reading other people’s recommendations and participating in online discussions. Thus, identifying and analyzing such discussion can provide even earlier signals about stock market trends and of possible rising interest towards specific stocks. In late 2020 and early 2021 the stock markets saw a sudden and unforeseen rise of certain stocks, as for instance the price of GME (see for instance https://finance.yahoo.com/quote/GME/) rose from around $4 USD in August 2020 to around $20 USD in early January 2021 and peaking at almost $350 USD later in January 2021. The cause of this sudden rise in the stock price of GME was traced back to social media and specifically to a subreddit called WallStreetBets (https://www.reddit.com/r/wallstreetbets/). Members of the subreddit were able to influence stock prices by creating a global movement of people buying certain stocks, causing the stock prices to rise. The aim of this project is to create an early signal system that will identify such social media discussions that may have impact on stock markets.

Measuring The Societal Impact Of Open Science (2015-2016)

Measuring The Societal Impact Of Open Science (2015-2016)

Project funded by the Ministry of Education and Culture (2015-2016)

Research Group

Senior Researcher Kim Holmberg (team leader)
Postdoctoral Researcher Fereshteh Didegah
Postdoctoral Researcher Timothy D. Bowman
University Lecturer Terttu Kortelainen
Project Researcher Julia Fomin

Abstract

The most cited definition of open science probably comes from Nielsen (2011), who defined it as “the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process.” Friesike and Schildhauer (2015) list the different forms or aspects of open science by interpreting the meaning of “open”. They list that the open science movement includes increased transparency of the research process (i.e. making data and tools openly available), increased collaboration by making the research process public and open for anyone to join, and efforts to make science more available to the public through 1) writing in a manner that is understandable even outside of academia, 2) including the public in the research process through “citizen science”, and 3) by ensuring open access to scientific literature. In addition to these Friesike and Schildhauer (2015) suggest that wider range of quantitative indicators of a wider range of impact can be incentivizing for researchers to make their research more accessible, adopting the open science ideology. These novel quantitative indicators of the impact that various research products have had and the attention they have received from a wider audience will be the focus of this research project as we develop methods and tools to measure the societal impact of Finnish research in Finland and beyond. This research will 1) investigate the current state of research in Finland using altmetric research methods and data, 2) develop data mining methods to capture the societal impact of Finnish research in Finland and beyond, and 3) develop novel quantitative indicators of research impact to incentivize researchers in adopting the open science movement.