A Network Topology Approach to Bot Classification

Automated social agents, or bots, are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%

You can't see what you can't see: Experimental evidence for how much relevant information may be missed due to Google's Web search personalisation

The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to 20% of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.Comment: paper submitted to the 11th Intl. Conf. on Social Informatics; revision corrects error in interpretation of parameter Psi/p in RBO resulting from discrepancy between the documentation of the implementation in R (https://rdrr.io/bioc/gespeR/man/rbo.html) and the original definition (https://dl.acm.org/citation.cfm?id=1852106) as per 20/05/201

A Socio-Informatic Approach to Automated Account Classification on Social Media

Automated accounts on social media have become increasingly problematic. We propose a key feature in combination with existing methods to improve machine learning algorithms for bot detection. We successfully improve classification performance through including the proposed feature.Comment: International Conference on Social Media and Societ

Kite-marks, standards and privileged legal structures; artefacts of constraint disciplining structure choices

As different countries and regions continue to develop policy and legal frameworks for social enterprises this paper offers new insights into the dynamics of legal structure choice by social entrepreneurs. The potential nodes of conflict between exogenous prescriptions and social entrepreneur’s own orientation to certain aspects of organization and what social entrepreneurs actually do in the face of such conflict is explicated. Kite-marks, standards and legal structures privileged by powerful actors are cast as political artefacts that serve to discipline the choices of legal structure by social entrepreneurs as they prescribe desirable characteristics, behaviours and structures for social enterprises. This paper argues that social enterprises should not be understood as the homogenous organisational category that is portrayed in government policy documents, kite-marks and privileged legal structures but as organisations facing a proliferation of structural forms which are increasingly rendered a governable domain (Nickel & Eikenberry, 2016; Scott, 1998) through the development of kite marks, funder / investor requirements and government policy initiatives. Further, that these developments act to prioritise and marginalise particular forms of social enterprises as they exert coercive, mimetic and normative pressures (DiMaggio & Powell, 1983) that act to facilitate the categorising of social enterprises in a way that strengthens institutional coherence and serves to drive the structural isomorphism (Boxenbaum & Jonsson, 2017; DiMaggio & Powell, 1983) of social enterprise activity. Whilst the actions of powerful actors work to maintain (Greenwood & Suddaby, 2006) the social enterprise category the embedded agency of social entrepreneurs acts to transform it (Battilana, Leca, & Boxenbaum, 2009). The prevailing Institutional logics (Ocasio, Thornton, & Lounsbury, 2017; Zhao & Lounsbury, 2016) that serve to both marginalise and prioritise those legal structures are used to present argument that the choice of legal structure for a social enterprise is often in conflict with the social entrepreneur's orientation to certain aspects of how they wish to organise. Where the chosen legal structure for a social enterprise is in conflict with the social entrepreneur's own organising principles as to how they wish to organise then this can result in the social entrepreneur decoupling (Battilana, Leca, & Boxenbaum, 2009) their business and/or governance practices from their chosen legal structure in order to resolve the tensions that they experience. Social entrepreneurs also experiencing the same tension enact a different response in that they begin to create and legitimate new legal structures on the margins of the social enterprise category through a process of institutional entrepreneurship (Battilana, Leca, & Boxenbaum, 2009; Hardy & Maguire, 2017)

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

Modelling Requirements for Content Recommendation Systems

This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the user is generating instances of different entities, depending on the role she has: a generator generates instances of a "post", while the receiver generates instances of a "reply". Therefore, we believe that when addressing Requirements Engineering (RE) for RS, it is necessary to distinguish these roles clearly. We aim to model an essential dynamic on OSN, namely that when a user creates (posts) content, other users can ignore that content, or themselves start generating new content in reply, or react to the initial posting. This dynamic is key to designing OSNs, because it influences how active users are, and how attractive the OSN is for existing, and to new users. We apply a well-known Goal Oriented RE (GORE) technique, namely i-star, and show that this language fails to capture this dynamic, and thus cannot be used alone to model the problem domain. Hence, in order to represent this dynamic, its relationships to other OSNs' requirements, and to capture all relevant information, we suggest using another modelling language, namely Petri Nets, on top of i-star for the modelling of the problem domain. We use Petri Nets because it is a tool that is used to simulate the dynamic and concurrent activities of a system and can be used by both practitioners and theoreticians.Comment: 28 pages, 7 figure