Like trainer, like bot? Inheritance of bias in algorithmic content moderation

The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.Comment: 12 pages, 3 figures, 9th International Conference on Social Informatics (SocInfo 2017), Oxford, UK, 13--15 September 2017 (forthcoming in Springer Lecture Notes in Computer Science

Measuring web ecology across Facebook, Twitter, blogs and online news: 2012 general election in South Korea

This study aimed to provide an empirical and analytical account of online information flow and web ecology in four platforms around the 2012 general election in South Korea. The study quantitatively examined the interrelationship of web ecology among relevant platforms—including online news, blogs, Twitter and Facebook—during election campaigns. In order to quantify the current political situations, this study employed network analysis and the model fitting method with co-occurrence web visibility data of political parties and their leaders in four platforms. The findings demonstrated to what extent web ecologies on four different online platforms during the given election reflect the country’s political situation. Comparing network centralizations across platforms, results showed that online news was the least biased media and Twitter was the most biased with the highest centralization scores. Although both the ruling party and the major opposition party—including the leaders of those two parties—had higher degree centrality scores than minor parties and their leaders in all platforms, some distinct features were observed in Twitter and Facebook due to their attributes. In addition, regressions and their residuals confirmed that web ecologies in four platforms, in terms of degree centrality, had been linearly expanded over time and showed their individual characteristics

Emerging Trends in Small Acts of Audience Engagement and Interruptions of Content Flows

This chapter develops a set of findings around audiences’ small-scale acts of engagement with media content made available through digital media technologies. We identify and discuss three articulations of these small acts: (1) one click engagement, (2) commenting and debating and (3) small stories. In contrasting them with more collaborative and convergent productive practices, we further conceptualise these engagements in relation to two main dimensions: effort and intentionality. Lastly, we suggest a conceptualisation of the outcome of these acts which we have labelled interruption. Content flows can be challenged, if not transformed, due to the volume of small acts, which is realised by the producing audiences as well as by mainstream media. Profound changes in the way information is produced and distributed are fuelled by small acts of engagement, and these trends are likely to continue into the futures this book speaks about