Temporal Network Analysis of Email Communication Patterns in a Long Standing Hierarchy

An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based on their hierarchical position. Although there have been prior studies of the relationship between hierarchy and voice, they tend to focus on more qualitative small-scale methods and do not account for structural aspects of the organisation. This paper develops large-scale computational techniques utilising temporal network analysis to measure the effect that organisational hierarchy has on communication patterns within an organisation, focusing on the structure of pairwise interactions between individuals. We focus on one major organisation as a case study - the Internet Engineering Task Force (IETF) - a major technical standards development organisation for the Internet. A particularly useful feature of the IETF is a transparent hierarchy, where participants take on explicit roles (e.g. Area Directors, Working Group Chairs). Its processes are also open, so we have visibility into the communication of people at different hierarchy levels over a long time period. We utilise a temporal network dataset of 989,911 email interactions among 23,741 participants to study how hierarchy impacts communication patterns. We show that the middle levels of the IETF are growing in terms of their dominance in communications. Higher levels consistently experience a higher proportion of incoming communication than lower levels, with higher levels initiating more communications too. We find that communication tends to flow "up" the hierarchy more than "down". Finally, we find that communication with higher-levels is associated with future communication more than for lower-levels, which we interpret as "facilitation". We conclude by discussing the implications this has on patterns within the wider IETF and for other organisations

Circuit breakers for multimedia congestion control

Real-time multimedia flows comprise a large, and increasing, fraction of the traffic on the Internet. An important subset of that traffic, primarily due to interactive applications, runs over UDP/IP, and requires applications to implement congestion control to ensure the stability of the network. The IETF is developing congestion control algorithms for such uses as part of the new WebRTC standards, but there is no standard algorithm that can be used at this time. We do not propose a congestion control algorithm. Rather, we propose a circuit breaker for RTP sessions that can detect when an application is causing excessive network congestion, and shut down the transmission. This can be used as an envelope within which congestion control algorithms can operate, providing a safety net to prevent congestion collapse. We present the RTP circuit breaker algorithm, and provide an initial performance evaluation to show that it performs as desired

DASHing Towards Hollywood

Data that underpins conference proceeding DASHing Towards Hollywood at ACM Multimedia Systems Conference, 12-15 June 2018. The dataset is too large to download (12GB) and can be requested using the 'Request Data' button above. The dataset is released under 3-clause BSD licence: http://www.opensource.org/licenses/BSD-3-Claus

Characterising the IETF through the lens of RFC deployment

Protocol standards, defined by the Internet Engineering Task Force (IETF), are crucial to the successful operation of the Internet. This paper presents a large-scale empirical study of IETF activities, with a focus on understanding collaborative activities, and how these underpin the publication of standards documents (RFCs). Using a unique dataset of 2.4 million emails, 8,711 RFCs and 4,512 authors, we examine the shifts and trends within the standards development process, showing how protocol complexity and time to produce standards has increased. With these observations in mind, we develop statistical models to understand the factors that lead to successful uptake and deployment of protocols, deriving insights to improve the standardisation process

Tracing Linguistic Markers of Influence in a Large Online Organisation

Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. Here, we investigate a similar question in a large, distributed, consensus-driven community – the Internet Engineering Task Force (IETF), a collaborative organisation that develops technical standards for the Internet. Our analysis, based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identifies key linguistic differences (e.g., certain LIWC categories, such as WE are positively correlated with high-influence). We also identify the differences in language use for the same person before and after becoming influential

The Web We Weave: Untangling the Social Graph of the IETF

The Internet Engineering Task Force (IETF) has developed many of the technical standards that underpin the Internet. The standards development process followed by the IETF is open and consensus-driven, but is inherently both a social and political activity, and latent influential structures might exist within the community. Exploring and understanding these structures is essential to ensuring the IETF’s resilience and openness. We use network analysis to explore the social graph of IETF participants, based on public email discussions and co-author relationships, and the influence of key contributors. We show that a small core of participants dominates: the top 10% contribute almost half (43.75%) of the emails and come from a relatively small group of organisations. On the other hand, we also find that influence has become relatively more decentralised with time. IETF participants also propose and work on drafts that are either adopted by a working group for further refinement or get rejected at an early stage. Using the social graph features combined with email text features, we perform regression analysis to understand the effect of user influence on the success of new work being adopted by the IETF. Our findings shed useful insights into the behavior of participants across time, correlation between influence and success in draft adoption, and the significance of affiliated organisations in the authorship of drafts