Social Network Analysis

Individualism and collectivism

By Pierre Bourdieu (London: Routledge, 2010), xxx + 607 pp. £15.99 paper. A combination of social theory, statistical data, illustrations, and interviews, Distinction: A Social Critique of the Judg...

Distinction: A Social Critique of the Judgement of Taste

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19p of microblogs contain mention of a brand. Of the branding microblogs, nearly 20p contained some expression of brand sentiments. Of these, more than 50p were positive and 33p were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy. © 2009 Wiley Periodicals, Inc.

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Twitter power: Tweets as electronic word of mouth

Online spaces that enable shared public interpersonal communications are of significant social, organizational, and economic importance. In this paper, a theoretical model and associated unobtrusive method are proposed for researching the relationship between online spaces and the behavior they host. The model focuses on the collective impact that individual information-overload coping strategies have on the dynamics of open, interactive public online group discourse. Empirical research was undertaken to assess the validity of both the method and the model, based on the analysis of over 2.65 million postings to 600 Usenet newsgroups over a 6-month period. Our findings support the assertion that individual strategies for coping with "information overload" have an observable impact on large-scale online group discourse. Evidence was found for the hypotheses that: (1) users are more likely to respond to simpler messages in overloaded mass interaction; (2) users are more likely to end active participation as the overloading of mass interaction increases; and (3) users are more likely to generate simpler responses as the overloading of mass interaction grows.The theoretical model outlined offers insight into aspects of computer-mediated communication tool usability, technology design, and provides a road map for future empirical research.

Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration

Asynchronous Learning Networks (ALNs) make the process of collaboration more transparent, because a transcript of conference messages can be used to assess individual roles and contributions and the collaborative process itself This study considers three aspects of ALNs: the design; the quality of the resulting knowledge construction process; and cohesion, role and power network structures The design is evaluated according to the Social Interdependence Theory of Cooperative Learning The quality of the knowledge construction process is evaluated through Content Analysis; and the network structures are analyzed using Social Network Analysis of the response relations among participants during online discussions In this research we analyze data from two three-monthlong ALN academic university courses: a formal, structured, closed forum and an informal, nonstructured, open forum We found that in the structured ALN, the knowledge construction process reached a very high phase of critical thinking and developed cohesive cliques The students took on bridging and triggering roles, while the tutor had relatively little power In the non-structured ALN, the knowledge construction process reached a low phase of cognitive activity; few cliques were constructed; most of the students took on the passive role of teacher-followers; and the tutor was at the center of activity These differences are statistically significant We conclude that a well-designed ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking

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Network analysis of knowledge construction in asynchronous learning networks

The asymmetry of activity in virtual communities is of great interest. While participation in the activities of virtual communities is crucial for a community's survival and development, many people prefer lurking, that is passive attention over active participation. Often, lurkers are the vast majority. There could be many reasons for lurking. Lurking can be measured and perhaps affected by both dispositional and situational variables. This project investigates social and cultural capital, situational antecedents of lurking and de-lurking. We propose a novel way of measuring such capital, lurking, and de-lurking. We try to figure out what are the triggers to active participation. We try to answer this by mathematically defining a social communication network of activities in authenticated discussion forums. Authenticated discussion forums provide exact log information about every participant's activities and allow us to identify lurkers that become first time posters. The proposed social communication network approach (SCN) is an extension of the traditional social network methodology to include, beyond human actors, discussion topics (e.g. Usenet newsgroups threads) and subjects of discussions (e.g. Usenet groups) as well. In addition, the social communication network approach distinguishes between READ and POST link types. These indicate active participation on the part of the human actor. We attempt to validate this model by examining the SCN using data collected in a sample of 82 online forums. By analyzing a graph structure of the network at moments of initial postings we verify several hypotheses about causes of de-lurking and provide some directions towards measuring active participation in virtual communities.

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De-lurking in virtual communities: a social communication network approach to measuring the effects of social and cultural capital

Jojoba Israel is a world-leading producer of Jojoba products, whose orchards are covered with sensors that collect soil moisture data for monitoring plant needs at real-time. Based on these data, the company’s agronomist defines a weekly irrigation plan. In addition, data on weather, irrigation, and yield are recorded from other sources (e.g. meteorological station and irrigation-plan records). However, so far, there has been no attempt to use the entire set of collected data to reveal insights and interesting relationships between different variables, such as soil, weather, irrigation characteristics, and resulting yield. By integrating and utilizing data from different sources, our research aims at using the collected data not only for monitoring and controlling the crop, but also for predicting irrigation recommendations. In particular, a dataset was constructed by integrating data collected over almost two years from 22 soil-sensors spread in four major plots (which are divided into 28 subplots and eight irrigation groups), from a meteorological station, and from actual irrigation records. Different regression and classification algorithms were applied on this dataset to develop models that were able to predict the weekly irrigation plan as recommended by the agronomist. The models were developed using eight different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Gradient Boosted Regression Trees, with 93% accuracy, and the best classification model was the Boosted Tree Classifier, with 95% accuracy (on the test-set). Data that were not contributing to the model prediction success rate were identified as well. The resulting model can significantly facilitate the agronomist’s irrigation planning process. In addition, the potential of applying machine learning on the company data for yield and disease prediction is discussed.

Gilad Ravid

Papers

Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration

Network analysis of knowledge construction in asynchronous learning networks

De-lurking in virtual communities: a social communication network approach to measuring the effects of social and cultural capital

Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge

Wikibooks in higher education