This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

Artificial intelligence (AI) has the potential to unlock transformative economic, social and environmental opportunities for Australia. The potential for public benefit is significant, provided the development, adoption and use of AI is governed in a safe, responsible and sustainable manner. Governing AI in this way underpins community trust and stakeholder support and works to retain a social license. Importantly, good governance of AI also increases the likelihood that organisations will implement and scale up AI effectively and successfully. In other words, good governance creates a virtuous cycle whereby support for the widespread investment in and adoption of AI is maintained, and the transformative benefits of AI are more likely to be realised both at a business and societal level.

Researchers have used social media data to estimate various macroeconomic indicators about public behaviors, mostly as a way to reduce surveying costs. One of the most widely cited economic indicator is consumer confidence index (CCI). Numerous studies in the past have focused on using social media, especially Twitter data, to predict CCI. However, the strong correlations disappeared when those models were tested with newer data according to a recent comprehensive survey. In this work, we revisit this problem of assessing the true potential of using social media data to measure CCI, by proposing a robust non-parametric Bayesian modeling framework grounded in Gaussian Process Regression (which provides both an estimate and an uncertainty associated with it). Integral to our framework is a principled experimentation methodology that demonstrates how digital data can be employed to reduce the frequency of surveys, and thus periodic polling would be needed only to calibrate our model. Via extensive experimentation we show how the choice of different micro-decisions, such as the smoothing interval, various types of lags etc. have an important bearing on the results. By using decadal data (2008-2019) from Reddit, we show that both monthly and daily estimates of CCI can, indeed, be reliably estimated at least several months in advance, and that our model estimates are far superior to those generated by the existing methods.

Its chief economist has hinted that analysis of people's music choices could be a useful tool and give a "window on their soul". In a speech about new possibilities in data analytics, Andy Haldane said data on downloads from Spotify had been used to gain an insight into people's mood. Book lists, TV choices and even computer games could also be used to gauge consumer confidence. He said: "To give one recent example, data on music downloads from Spotify has been used, in tandem with semantic search techniques applied to the words of songs, to provide an indicator of people's sentiment." He said the results did at least as well in tracking consumer spending as the well-regarded Michigan survey of consumer confidence in the US.

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contempora- neous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The re- sults highlight the potential of text streams as a substi- tute and supplement for traditional polling. consumer confidence and political opinion, and can also pre- dict future movements in the polls. We find that temporal smoothing is a critically important issue to support a suc- cessful model.