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The purpose of this paper is to demonstrate a spatial model of population vulnerability (VI) capable of identifying areas of high emergency service demand (ESD) during extreme heat events (EHE).

An index of population vulnerability to EHE was developed from a literature review. Threshold temperatures for EHE were defined using local temperatures, and indicators of increased morbidity. Spearman correlations determined the strength of the relationship between the VI and morbidity during EHE. The VI was mapped providing a visual guide of risk during EHE. Future changes in population vulnerability based on future population projections (2020-2030) were mapped.

The VI can be used to explain the spatial distribution of ESD during EHE. Mapping future changes in population density/demography indicated several areas currently showing high risk will continue to show increased risk.

The limitations include using outdoor temperatures to determine health-related thresholds. Due to data restrictions three different measures of morbidity were used and aggregated to postal areas.

Identifying areas of increased service demand during EHE allows the development of proactive as-well-as reactive responses to heat. The model uses readily available data, is replicable in larger urban areas.

The model allows emergency service providers to work with high risk communities to build resilience to heat exposure and subsequently save lives.

To the authors’ knowledge this triangulated approach using heat thresholds, ESD and projected changes in risk in a spatial framework has not been presented to date.

The death was announced on 21st December 1964 of Mr H. R. Verry, who was known to many members of Aslib. Up to his retirement in 1963 Mr Verry was Adviser on Photographic and Allied Processes to the O & M Section of the Treasury. He was the author of a number of textbooks on photographic reproduction techniques and often contributed to the book reviews in the Journal of Documentation.

BOOKS on Work Study and its related techniques written by British authors are a mere runnel when contrasted with the literary Mississippi which flows from the American presses. A new one is an event, so we are glad to welcome Michael Avery's volume on Methods Engineering which embodies some material previously contributed to this journal.

The purpose of this paper is to examine one of the largest data sets on the hashtag use of #fakenews that comprises over 14m tweets sent by more than 2.4m users.

Tweets referencing the hashtag (#fakenews) were collected for a period of over one year from January 3 to May 7 of 2018. Bot detection tools were employed, and the most retweeted posts, most mentions and most hashtags as well as the top 50 most active users in terms of the frequency of their tweets were analyzed.

The majority of the top 50 Twitter users are more likely to be automated bots, while certain users’ posts like that are sent by President Donald Trump dominate the most retweeted posts that always associate mainstream media with fake news. The most used words and hashtags show that major news organizations are frequently referenced with a focus on CNN that is often mentioned in negative ways.

The research study is limited to the examination of Twitter data, while ethnographic methods like interviews or surveys are further needed to complement these findings. Though the data reported here do not prove direct effects, the implications of the research provide a vital framework for assessing and diagnosing the networked spammers and main actors that have been pivotal in shaping discourses around fake news on social media. These discourses, which are sometimes assisted by bots, can create a potential influence on audiences and their trust in mainstream media and understanding of what fake news is.

This paper offers results on one of the first empirical research studies on the propagation of fake news discourse on social media by shedding light on the most active Twitter users who discuss and mention the term “#fakenews” in connection to other news organizations, parties and related figures.