Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Networked but Commodified: The (Dis)Embeddedness of Digital Labour in the Gig Economy

This article investigates the (dis)embeddedness of digital labour within the remote gig economy. We use interview and survey data to highlight how platform workers in Southeast Asia and Sub-Saharan Africa are normatively disembedded from social protections through a process of commodification. Normative disembeddedness leaves workers exposed to the vagaries of the external labour market due to an absence of labour regulations and rights. It also endangers social reproduction by limiting access to healthcare and requiring workers to engage in significant unpaid ‘work-for-labour’. However, we show that these workers are also simultaneously embedded within interpersonal networks of trust, which enable the work to be completed despite the low-trust nature of the gig economy. In bringing together the concepts of normative and network embeddedness, we reconnect the two sides of Polanyi’s thinking and demonstrate the value of an integrated understanding of Polanyi’s approach to embeddedness for understanding contemporary economic transformations

ICTs for indigenous knowledge preservation

Public libraries in South Africa engage with local communities to preserve indigenous knowledge. This involves teaching them to use ICT tool

Crowdsourced land rights

Crowdsourcing initiatives can encourage and support citizens to directly capture and maintain information about land rights. A database of crowdsourced land rights can improve security of tenure for the poores