Auto-versioning file systems offer a simple and reliable interface to document change control. The implicit versioning of documents at each write access catches the whole evolution of a document, thus supporting regulatory compliance rules. Most existing file systems work on low abstraction levels and track the document evolution on their binary representation. Higher-level differencing tools allow for a far more meaningful change-tracking, though.In this paper, we present an auto-versioning file system that is able to handle files depending on their file type. This way, a suitable differencing tool can be assigned to each file type. Our approach supports regulatory compliant storage as well as the archiving of documents.
Hate speech expresses prejudice and discrimination based on actual or perceived innate characteristics such as gender, race, religion, ethnicity, colour, national origin, disability or sexual orientation. Research has proven that the amount of hateful messages increases inevitably on online social media. Although hate propagators constitute a tiny minority—with less than 1% participants—they create an unproportionally high amount of hate motivated content. Thus, if not countered properly, hate speech can propagate through the whole society. In this paper we apply agent-based modelling to reproduce how the hate speech phenomenon spreads within social networks. We reuse insights from the research literature to construct and validate a baseline model for the propagation of hate speech. From this, three countermeasures are modelled and simulated to investigate their effectiveness in containing the spread of hatred: Education, deferring hateful content, and cyber activism. Our simulations suggest that: (1) Education consititutes a very successful countermeasure, but it is long term and still cannot eliminate hatred completely; (2) Deferring hateful content has a similar—although lower—positive effect than education, and it has the advantage of being a short-term countermeasure; (3) In our simulations, extreme cyber activism against hatred shows the poorest performance as a countermeasure, since it seems to increase the likelihood of resulting in highly polarised societies.
Hate speech expresses prejudice and discrimination based on personal characteristics such as race or gender. Research has proven that the amount of hateful messages increases on online social media. If not countered properly, the spread of hatred can overwhelm entire societies. This paper proposes a multi-agent model of the spread of hatred. We reuse insights from previous research to construct and validate a baseline model. From this, three countermeasures are modelled and simulated to investigate their effectiveness in containing the spread of hatred: (1) The long-term measure of education is very successful, but it still cannot eliminate hatred completely; (2) Deferring hateful content has a similar positive effect with the advantage of being a short-term countermeasure; (3) Extreme cyber activism against hatred can worsen the situation and even increase the likelihood of high polarisation within societies.
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