Semantic Network Analysis of Chinese Social Connection (“Guanxi”) on Twitter

About two months ago, a paper of ours with the above title appeared on Frontiers in Digital Humanities (Big Data).

This paper has emerged from my former MSc student at the Oxford Internet Institute, Pu Yan, who is currently working on her PhD in our department.

In this paper we combined a network analysis tool with computational linguistic methods to understand the differences in the ways that Guanxi is conceptualized in two different Chinese cultures (Mainland vs Taiwan, Hong Kong, and Macau).

What I like about this paper is the discussion of the results rather than anything else. Pu, with her great domain knowledge, interprets the results in a very insightful way.

The paper is available here and the abstract says:

Guanxi, roughly translated as “social connection,” is a term commonly used in the Chinese language. In this study, we employed a linguistic approach to explore popular discourses on guanxi. Although sharing the same Confucian roots, Chinese communities inside and outside Mainland China have undergone different historical trajectories. Hence, we took a comparative approach to examine guanxi in Mainland China and in Taiwan, Hong Kong, and Macau (TW-HK-M). Comparing guanxi discourses in two Chinese societies aim at revealing the divergence of guanxi culture. The data for this research were collected on Twitter over a three-week period by searching tweets containing guanxi written in simplified Chinese characters (关系) and in traditional Chinese characters (關係). After building, visualizing, and conducting community detection on both semantic networks, two guanxi discourses were then compared in terms of their major concept sub-communities. This study aims at addressing two questions: Has the meaning of guanxi transformed in contemporary Chinese societies? And how do different socio-economic configurations affect the practice of guanxi? Results suggest that guanxi in interpersonal relationships has adapted to a new family structure in both Chinese societies. In addition, the practice of guanxi in business varies in Mainland China and in TW-HK-M. Furthermore, an extended domain was identified where guanxi is used in a macro-level discussion of state relations. Network representations of the guanxi discourses enabled reification of the concept and shed lights on the understanding of social connections and social orders in contemporary China.

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What’s the state of the art in understanding Human-Machine Networks?

About a month ago, we finished our 2-year long EC-Horizon2020 project on Human-Machine Networks (HUMANE). The first task of this project was to perform a systematic literature review to see what the state of the art in understanding such systems is.

The short answer is that we do not know much! And what we know is not very cohesive. In other words, design, development, and exploration of human-machine systems have been done mostly through trial and error and there has not been much theory or systematic thinking involved.

We wrote a review paper to report on our systematic exploration of the literature. It took us nearly 18 months to finally get the paper published, but it was worth every second waiting as we managed to get it out at the ACM Computing Survey, which has the highest impact factor among all the journals in Computer Science.

Here you can read the paper.

And the abstract says:

In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

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The OII Colloquia

I am very happy to announce our new series of seminars at the Oxford Internet Institute (OII), called “The OII Colloquia (TOC)“.

The OII Colloquia bring senior speakers from other departments at the University of Oxford to the Oxford Internet Institute to spark conversation around the Internet and society.

The word Colloquia (sing.: Colloquium) comes from the Latin word “Colloquy” meaning “Conversation”. Today, we often use the term to describe departmental seminars with a general topic and audience. 

https-%2f%2fcdn-evbuc-com%2fimages%2f26124578%2f154856160921%2f1%2foriginalThe OII Colloquia, however, come closer to the original sense of the word: through this series of events we aim to initiate conversations and strengthen our ties with scholars at other departments of the University of Oxford, around topics of shared interest. They should be considered as a trigger for long-lasting collaborations between the OII and the speakers’ own departments.

TOC are held twice a term (weeks 2 and 7) on Thursdays from 17:15 to 18:45 in an interactive and stimulating environment at the Oxford Internet Institute, 1 St Giles OX1-3JS open to the public (upon registration).

New Paper: Personal Clashes and Status in Wikipedia Edit Wars


Originally posted on HUMANE blog by Milena Tsvetkova.

Our study on disagreement in Wikipedia was just published in Scientific Reports (impact factor 5.2). In this study, we find that disagreement and conflict in Wikipedia follow specific patterns. We use complex network methods to identify three kinds of typical negative interactions: an editor confronts another editor repeatedly, an editor confronts back an equally experienced attacker, and less experienced editors confront someone else’s attacker.

Disagreement and conflict are a fact of social life but we do not like to disclose publicly whom we dislike. This poses a challenge for scientists, as we rarely have records of negative social interactions.

To circumvent this problem, we investigate when and with whom Wikipedia users edit articles. We analyze more than 4.6 million edits in 13 different language editions of Wikipedia in the period 2001-2011. We identify when an editor undoes the contribution by another editor and created a network of these “reverts”.

A revert may be intended to improve the content in the article but may also indicate a negative social interaction among the editors involved. To see if the latter is the case, we analyze how often and how fast pairs of reverts occur compared to a null model. The null model removes any individual patterns of activity but preserves important characteristics of the community. It preserves the community structure centered around articles and topics and the natural irregularity of activity due to editors being in the same time zone or due to the occurrence of news-worthy events.

Using this method, we discover that certain interactions occur more often and during shorter time intervals than one would expect from the null model. We find that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor beyond what would be needed just to improve and maintain the encyclopedia objectively. In addition, we analyze the editors’ status and seniority as measured by the number of article edits they have completed. This reveals that editors with equal status are more likely to respond to reverts and lower-status editors are more likely to revert someone else’s reverter, presumably to make friends and gain some social capital.

We conclude that the discovered interactions demonstrate that social processes interfere with how knowledge is negotiated. Large-scale collaboration by volunteers online provides much of the information we obtain and the software products we use today. The repeated interactions of these volunteers give rise to communities with shared identity and practice. But the social interactions in these communities can in turn affect knowledge production. Such interferences may induce biases and subjectivities into the information we rely on.