Data privacy and public data: New theory and empirical evidence
Australian National University, Australia
Relevance & Research Question:
Attitudes towards data privacy, either with regards to one’s own data or data more broadly, has the potential to be one of the major factors in the functioning of commercial markets and the effective operation of government and public policy. However, there are legitimate concerns about that data being used in ways that lead to negative outcomes for us as individuals, for groups that we identify with, or for society more broadly. Governments are continuously attempting to balance those costs and benefits, and commercial organisations need to be wary of losing their social licence with regards to data. Public attitudes towards data privacy and data governance is a key input into this balancing, and there is a growing social science literature on how these attitudes are shaped, how they vary between and within populations, and the impact they have on decision making. This paper consolidates existing literature and presents new empirical research (observational and experimental) with the aim of developing a theoretical model of data privacy. This predictive model builds on the behavioural economics literature and combines elements of risk, trust, perceived benefits, perceived costs, and behavioural biases.
Methods & Data:
The new data presented in this paper is based on a series of survey questions (experimental and attitudinal) fielded on the Life in Australia panel. Primary data collection is supported by analysis of survey data from comparable jurisdictions (primarily New Zealand, the US and Europe)
The main finding is that the model is shown to predict behaviour with regards to data linkage consent, public health data, and the consumer data right/open banking legislative reforms. Specifically, we show the importance of trust in government, the interaction that trust has with risk preference, the complicated impact of framing, and the importance of issue salience. We also demonstrate the instability of preferences through time.
While primarily based on Australian data, this is the first theoretical model of data privacy that (a) i built on experimental data from a representative panel (b) utilises behavioural insights and biases and (c) is shown to predict actual behaviour.
What you read is who you support? Online news consumption and political preferences
1University of Mannheim, Germany; 2respondi, Université Paris Nanterre, France
Relevance & Research Question: Passively measured digital trace data (e.g., from social media platforms or web browsing logs) are increasingly used in the social sciences as a cost-efficient alternative or as a complement to surveys. In this context, particularly augmenting survey data with information from respondents' online activities represents a promising direction to utilize the advantages of both worlds. However, extracting meaningful measurements from digital traces for substantive research is a challenging task. In this study, we use natural language processing techniques (NLP) to classify respondents based on the news content they consumed online and study the link between individuals’ political preferences and their online behavior.
Methods & Data: We augment survey data with web browsing logs by members of an online panel running in three countries (France, Germany and the UK). Data were collected during four weeks in May 2019, covering the European elections. Respondents answered questions about their voting behavior and political preferences in two surveys, one before and one after the election. In addition, respondents gave consent to having their online activities on their personal computers and mobile devices recorded. We extract information about news content consumed by respondents and search queries made to online search engines from the web logs. To analyze this content, we use natural language processing techniques (BERT; Bidirectional Encoder Representations from Transformers). We then model respondents’ political preferences and voting behavior using features from the content consumed online.
Results: Preliminary results indicate that the content of news consumed is a good predictor of voting behavior and political preferences and outperforms information that merely summarize visits to news and other websites. Overall, however, records of online activities do not seem to predict voting behavior and political preferences in an almost deterministic way.
Added Value: Our results add to the debate of selective news exposure and changes in political engagement due to Internet use. Moreover, they confirm previous findings regarding the limited effect size of internet use and selective news exposure on political behaviors and preferences. Overall, it seems that online societies may not be as fragmented as some early commentators postulated.
How do news and events impact climate anxiety and how are people reacting?
Relevance & Research Question: Global climate change continues to increase in visibility through observable environmental events, news, or political attention. As a result, psychological and mental health implications have begun to be considered in academic and practitioner circles. Awareness of the threat of climate change has led to an increase in “eco-anxiety” or “climate anxiety” in which individuals feel stressed, worried, nervous, or distressed about the future on Earth. This research aims to add to the growing body of literature on climate anxiety by examining American-based social media posts with the intention of understanding what type of climate-related events, news stories or political activities impact expressions of climate anxiety. Additionally, this research seeks to examine if people suggest taking any household, local, or national actions in light of climate anxiety, such as reducing consumption, switching to green products, or advocating and voting for climate friendly politicians.
Methods & Data: With access to Twitter and Reddit data through Brandwatch Consumer Research software, close to 50,000 posts about climate anxiety were collected from January 2016 through October 2019 (45 months) geolocated in the United States. An exploratory data-led approach will be taken with a sample of this data to establish themes and actions being discussed. Once these themes and actions are established, automated text analysis will be used to populate the categories against data from the full dataset such that they will continue to segment new conversation as it happens. This will allow revisiting the data in the future to become aware of any longitudinal changes in people’s opinions and behaviour.
Results: The analysis will be completed early 2020.
Added Value: The research intends to create a framework for understanding how social data can be used to examine drivers of climate anxiety, as well as possible tipping points that create impetus for personal, social, or political action among those most worried about the impact of climate change on our future. Additionally, understanding tipping points can aid pro-climate politicians and activists to capitalise on events and stories that most trigger action.