Submission for the Queensland Government Parliamentary Inquiry into personal health promotion interventions using telephone and web-based technologies

Telephone and web-based technologies such as SMS, smartphone apps, gamification, online/mobile games, online quizzes and tools can be used in personal health interventions in two ways: health promotion or social marketing. In response to the Queensland government's call for submissions to the parliamentary inquiry, a social marketing and design submission from four of the faculties at Queensland University of Technology was submitted. There appears to be a great deal of confusion in government circles about the terms ‘social marketing’ and ‘health promotion’ and often they are used interchangeably when they are actually significantly different approaches. Social marketing is the science and practice of behaviour change and involves goods and services that offer a value proposition, and which incentivises citizens to change their behaviour voluntarily. However, social marketing is often mistakenly used to describe advertising and communication or social media marketing. This submission contains an overview of how technology interventions need to be implemented to be successful, provides examples of the evidence that telephone and web-based interventions can effectively influence public health outcome. This submission poses seven critical factors.

Parallel streaming signature EM-tree: A clustering algorithm for web scale applications

The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.