Policy and population behavior in the age of Big Data

The dynamic evolution of collaborative networks in sustainable development: Untying impact of environmental policy in China using network-based text analysis approach

As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. The primary contents are bidirectional encoder representation from transformers-convolution neural network (BERT-CNN) sentiment tendency analysis, word frequency characteristic analysis, and semantic network analysis. The realism of the suggested framework is demonstrated by using the waste classification policy as an example. The findings indicate a substantial relationship between perceived subject participation and policy pilot areas and that perceived subject participation is repeating. On this premise, specific recommendations are made to encourage policy implementation.

Communicating the effectiveness and ineffectiveness of government policies and their impact on public support: a systematic review with meta-analysis

Low public support for government interventions in health, environment and other policy domains can be a barrier to implementation. Communicating evidence of policy effectiveness has been used to influence attitudes towards policies, with mixed results. This review provides the first systematic synthesis of such studies. Eligible studies were randomized controlled experiments that included an intervention group that provided evidence of a policy's effectiveness or ineffectiveness at achieving a salient outcome, and measured policy support. From 6498 abstracts examined, there were 45 effect sizes from 36 eligible studies. In total, 35 (N = 30 858) communicated evidence of effectiveness, and 10 (N = 5078) communicated evidence of ineffectiveness. Random effects meta-analysis revealed that communicating evidence of a policy's effectiveness increased support for the policy (SMD = 0.11, 95% CI [0.07, 0.15], p < 0.0001), equivalent to support increasing from 50% to 54% (95% CI [53%, 56%]). Communicating evidence of ineffectiveness decreased policy support (SMD = -0.14, 95% CI [-0.22, -0.06], p < 0.001), equivalent to support decreasing from 50% to 44% (95% CI [41%, 47%]). These findings suggest that public support for policies in a range of domains is sensitive to evidence of their effectiveness, as well as their ineffectiveness.

Real-world unexpected outcomes predict city-level mood states and risk-taking behavior

Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, using naturalistic datasets, how real-world unexpected outcomes predict mood state changes observable at the level of a city, in turn predicting changes in gambling behavior. By analyzing day-to-day mood language extracted from 5.2 million location-specific and public Twitter posts or 'tweets', we examine how real-world 'prediction errors'-local outcomes that deviate positively from expectations-predict day-to-day mood states observable at the level of a city. These mood states in turn predicted increased per-person lottery gambling rates, revealing how interplay between prediction errors, moods, and risky decision-making unfolds in the real world. Our results underscore how social media and naturalistic datasets can uniquely allow us to understand consequential psychological phenomena.

The effectiveness of public health advertisements to promote health: a randomized-controlled trial on 794,000 participants

As public health advertisements move online, it becomes possible to run inexpensive randomized-controlled trials (RCTs) thereof. Here we report the results of an online RCT to improve food choices and integrate exercise into daily activities of internet users. People searching for pre-specified terms were randomized to receive one of several professionally developed campaign advertisements or the “status quo” (ads that would otherwise have been served). For 1-month pre-intervention and post-intervention, their searches for health-promoting goods or services were recorded. Our results show that 48% of people who were exposed to the ads made future searches for weight loss information, compared with 32% of those in the control group—a 50% increase. The advertisements varied in efficacy. However, the effectiveness of the advertisements may be greatly improved by targeting individuals based on their lifestyle preferences and/or sociodemographic characteristics, which together explain 49% of the variation in response to the ads. These results demonstrate that online advertisements hold promise as a mechanism for changing population health behaviors. They also provide researchers powerful ways to measure and improve the effectiveness of online public health interventions. Finally, we show that corporations that use these sophisticated tools to promote unhealthy products can potentially be outbid and outmaneuvered.

People who see specific health-promoting messages after searching online for weight-related terms are more likely to subsequently search for information on weight loss interventions. A team led by Elad Yom-Tov from Microsoft Research Israel in Herzeliya conducted a randomized trial involving 794,000 users of the Bing search engine who queried terms related to weight, diet, and exercise. Randomly chosen subjects were shown advertisements designed to promote healthy living, while all other users were shown standard ads. The researchers found that 48% of people exposed to the health-promoting advertisements made searches within the next month for weight loss information, compared with only 32% of those in the control group. The findings suggest that targeted online messaging can help change population health behaviors.