Exploring Ethos in Contemporary Ghana

In this article, we discuss contemporary Ghanaian ethos reflecting on female sexual behavior as a discursive construction that shifts and changes across time and space. Borrowing from Nedra Reynold’s concept of ethos as a location, we examine the various social and discourse spaces of different rhetors on female sexual behavior in Ghana and how each establishes ethos through identity formations and language use from various positions of authority. With multiethnic, multilingual, and multiple religious perspectives within the Ghanaian population, how does ethos and moral authority speak persuasively on female sexual behavior? We examine contemporary discourses governing normative female sexual behavior and presentation as revealed in both proverbs and social media to drive the discussion toward how these discourses of female sexual behavior and ethos are discursively constructed in contemporary Ghanaian society.

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Location and Language Use in Social Media

10.3115/v1/w14-2504 ◽

2014 ◽

Author(s):

Ed Chi

Keyword(s):

Social Media ◽

Language Use

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Real-time spatio-temporal event detection on geotagged social media

Journal Of Big Data ◽

10.1186/s40537-021-00482-2 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yasmeen George ◽

Shanika Karunasekera ◽

Aaron Harwood ◽

Kwan Hui Lim

Keyword(s):

New York ◽

Social Media ◽

Event Detection ◽

Detection System ◽

Time And Space ◽

Social Media Data ◽

Event Time ◽

Spatio Temporal ◽

Geographical Space ◽

Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

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