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- Representational Communication Networks
- Communication Traffic Networks
Technocentric network managers see network nodes as hardware/software units linked by voice, video, text, and numerical data traffic, while human communication network analysts focus on people as nodes. They use various means of communication to link with one another. Communication scientists study human communication networks ranging from nodes at the intrapersonal level through levels of increasing aggregation: individuals, groups, departments, organizations, nations, and cultures. They also map networks of words at various levels.
One kind of human communication network analysis is representational, using indirect communication data projected into a medium. An example is the network of presidential cabinet members who appear within news stories in a large collection. The second kind indexes actual message traffic among nodes. An example is a network of who sends e-mail to whom.
Outside the academic communication discipline, the variations in the use of the term communication network can be perplexing. Therefore, the discussion can begin by excluding the technocentric network: for example, when a manager of a cell phone company considers the idea of a communication network. In this context, he or she envisions a radio network covering a large number of local land areas, each called a “cell” and having a fixed transceiver base station linked to an array of 360-degree antennae, usually mounted on a tower. These base stations link to each other to provide coverage over a wide area, such as a country or a larger global region. This network enables portable transceivers, such as mobile phones, netbooks, laptops, and 3G, 4G, or GSM-enabled tablets to exchange information with other units as they move around in the coverage network. This network description does not mention people. Rather, this technocentric view of a communication network sees hardware/ software nodes linked by exchange of information with other hardware/software nodes.
In contrast, when a cell phone user thinks of a communication network, he or she is more likely to envision the people she talks and texts with and who do so with one another. This view is of a human communication network. Networks with human nodes are the primary concern of human communication scholars.
A brief definition of human communication is helpful in making sense of the variety of human networks researchers have studied. Communication is the exchange or representation of spoken, textual, or nonverbal symbolic content in various audio, visual, and textual forms within and across nodes at various levels of analysis: intrapersonal, individual, group, community, organization, nation, and cross-national culture. The producers of communication content and its consumers are embedded, along with networks of content elements such as words and images, in communication networks at these various levels of analysis. Over time, layers of the network screw in and out of each other, based on shifting sentiments and other causes of cross-level structural inversions.
Representational Communication Networks
Two major categories of human communication networks are (1) representational and (2) traffic networks. Representational networks reveal communication relationships among nodes based on various kinds of communication equivalence. Nodes appear connected when indirect associations are projected into a medium. For example, organizations mentioned together in news stories about a topic enable a representation of a network among them. In contrast, traffic networks are created by measurement of direct message flows among nodes. Often researchers use a representational network as a surrogate for a traffic network, particularly when traffic data are not readily available.
The most basic representational networks are comprised of thoughts and emotions that occur at the intrapersonal level. Words and images are linked together by sequences of thoughts. These are knowable to others through indirect means, such as by asking the individual to describe inner networks. These are indirect representations of networks rather than direct measures of electrochemical communication among clusters of neurons. Other kinds of intrapersonal communication networks are knowledge networks, which individuals produce when asked questions of fact.
Other representational communication network studies record what thoughts first come to mind when individuals are given priming words or questions, to which they respond with open-ended answers. For example, “When you think of ‘arc,’ what comes to mind? What else? Anything else?” Studies have recorded individuals’ word associations to thousands of words, then represented word association networks.
Sometimes this kind of analysis is tailored to a particular domain, such as names of companies or of election candidates. Researchers ask organizational members what comes to mind when they think of the corporate mission statement, then code the similarity of responses among each pair of respondents and network analyze not the words but the respondents as nodes.
Based on naturally occurring communication, investigators have “text mined” words that co-occur across people’s e-mail or other forms of textual messages, such as tweets, and created aggregated network representations of words. They have also studied the similarities of whole semantic networks across discussion forum posts for pairs of individual authors who have not yet communicated but have the potential to do so.
There have been three main ways of indexing word relationships. The first extracted post–reply pairs from a discussion forum on a computer bulletin board system, as these emerged in the late 1970s. Then, concepts appearing in the post were counted as linked with the concepts appearing in the reply. All overlapping sets of posts and replies were coded. Concepts were then network-analyzed and an optimal message generator was used to produce an experimental message designed to move a new message closest to the center of the discussion.
The next way of indexing word relationships is a “bag of words” model that treats all words in a document as equally linked; communication networks are constructed across the “bags.” A more refined third way is based on close proximity of words, counting as linked word pairs appearing within three word positions on either side of each word throughout the text.
At such aggregate text levels, researchers have also mined textual corpora for types of words such as names of people, places, objects, resources, or issues with their relative co-occurrence defining their links. Analyzing an ancient network, researchers network-analyzed persons co-mentioned in the letters of the Roman emperor Cicero. Using more recent data, researchers have extracted names of terrorists along with locations, weapons, incidents, and targets. One study analyzed the word associations to “Arab” across television news stories, while another mapped word networks surrounding “homeless.” Communication scholars have used software to extract the names of presidential cabinet members that occurred closely together across many news stories and analyzed the centrality of the president in the cabinet’s network structure from time slices synchronized with Gallup approval polls.
Another kind of representational network akin to a word network is an image network. Researchers have examined the co-occurrence of pictorial or video elements. Some research has linked word networks to such networks of visuals. Other research has linked brain waves with networks of changing visual elements of television advertisements. Representational communication networks also exist at the organizational department level. A study of a network of college departments’ collaboration was conducted, based on the co-occurrences of department names in news stories. Similarly, interorganizational networks have been extracted from organizations’ names appearing closely in news stories about the British Petroleum Gulf oil spill over time in relation to story sentiment. Other interorganizational research has mapped the links among Fortune 100 organizations based on their use of some of the same advertising and public relations firms. Interorganizational networks have been represented from data on corporate mergers and acquisitions in the information industries from the 1980s forward, studying the effects of convergence. Illustrating the national level of representational networks, researchers have conducted hyperlink or Webometric mining of a Webpage for links across different national Internet domains.
In scientometrics, scholars study links among scientific journals as nodes based on how often authors in one journal cite an article in another journal. Another form of representational scientific communication network uses coauthorship of articles to study collaboration networks among scientists. Other studies have mapped academic associations’ divisions, which represent different subdisciplines. For example, individuals’ co-memberships in multiple divisions of the International Communication Association (ICA) and of the National Communication Association (NCA) have been studied to represent the basic structure of the communication discipline through cluster analysis of the interdivisional network over time. As well, the content of the papers presented in different divisions have been network-analyzed to represent the concepts most central to the discipline. A study found only 10 communication concepts that all ICA divisions shared. Other representational scientific communication network research has mapped doctoral programs in communication as nodes based on hiring faculty from other communication departments.
Analyzing representational networks of entertainment and informational television programming, researchers have studied the networks of cable channels by aggregating audience ratings data from people meters that record what channel is watched before and after every other channel watched. The resulting communication network is of cable channels and also cable programs as nodes. Other researchers have measured the fictional communication networks of soap opera characters and linked variations to indicators of actual interpersonal communication networks in media markets, finding that viewers tended to watch soap networks with the opposite structure of interpersonal networks in their markets.
Communication Traffic Networks
The most frequently studied type of network has been communication traffic networks. Beginning in the late 1960s–early 1970s, researchers in the communication department at Michigan State University (MSU) studied communication traffic networks among individual nodes in the Department of Defense Office of Civil Preparedness, and in a parallel study, in the stock transaction processing unit of the Chase Manhattan bank. As well, a study was done of interpersonal communication networks among women in 25 Korean villages receiving an experimental treatment, an extensive set of government development programs, compared to 25 control villages.
In each of these studies, individual communication traffic networks were measured by self-report surveys. In the organizational studies, self-administered questionnaires with name rosters were completed by all organizational members in group settings. The same techniques were later used in a number of organizations as part of the ICA audit program that grew out of the MSU organizational network studies. Less often, individuals have been asked to keep logs of their communication and record names and other data each time they communicate.
One study used electronic logs from an organization in which each member had a unique telephone number. The network of individuals’ communication was measured by what telephone numbers called what other numbers, recorded by the PBX switch. The Electronic Information Exchange Study (EIES), which the National Science Foundation funded for several years in the mid-1970s, selected scientists to use e-mail, group discussion lists, instant messages, and file sharing provided from a dedicated mainframe computer reachable from the ARPANET or from dial-up modems; scientists’ communication networks were measured from log data and productivity measured from reports of publications. One of the groups was comprised of social scientists conducting network analysis research. Soon after the project ended, they launched the International Network for Social Network Analysis (INSNA).
In the 1980s, researchers studied e-mail networks among individuals in an organization and the networks of words in the content in order to overlay the two kinds of networks and see how they evolved over time. When an unexpected natural event occurred—a crisis that threatened the existence of the organization—researchers were able to examine changes in who-to-whom network structure as it spiked toward randomness for one month when the crisis was announced, as well as oscillations of the word networks that continued six months longer. Privacy issues are important determinants of how much access researchers have to logs of e-mail and other communication that people assume to be private. While in the United States it is legal for an organization to monitor complete contents of internal, electronically mediated communication without consent from the participants, privacy policies in other countries, particularly in Europe, may require individuals to give their consent.
Accordingly, in a study of a Ford Motor Company automobile engineering unit of 1,900 engineers from the United States and Germany, only 38 gave their consent for automated capture of e-mails about innovations. Nevertheless, because of a corporate norm of replying to and forwarding e-mails with long chains of earlier e-mails intact, the researchers were able to reconstruct the network among the 1,900 engineers for two years prior to the study and for two years during it. To protect privacy, the researchers had a third party convert all personal names to numbers as they studied the diffusion of word networks and sentiment across who-to-whom networks about the innovations over time.
Mining Twitter networks, researchers have measured the density of followers’ tweet networks in relation to leaders’ self-oriented versus other-oriented tweet content. Some researchers study face-to-face communication networks among individuals by having them wear “sociometric badges” that contain small radio transceivers. When two or more badges are within range, a wireless computer tracks these occurrences so the traffic network can be analyzed.
In a macro-level network traffic study, researchers have examined pairs of nations and mapped their networks based on the millions of minutes of telephone call traffic per year over several decades, using data recorded by international telephone traffic monitoring organizations and made available by TeleGeography. Other researchers have examined intercultural networks, mapping cultures as nodes based on the set of lists contained in headers of posts to cultural discussion lists on USNET.
Across both representational and traffic network studies described, a number have sliced networks by time intervals to study communication network change. Other advanced procedures have used statistical modeling developed specifically for network analysis hypothesis testing that removes dependency problems due to links among nodes, such as exponential random graph models (ERGM). One such work of note was specifically focused on testing hypotheses about communication networks derived from a variety of theories about the conditions under which different network structures occur.
The advanced communication network design of network layering merits additional attention; this is another fruitful way to gain increased knowledge from communication networks over time, particularly when the nodes are geocoded for location. Overlaying the word and visual networks onto the synchronous individual networks reveals how various networks of words and images produced in some locations activate networks in others. For example, researchers have extracted location-coded tweets and synchronized these with President Barack Obama’s State of the Union address. They have also synchronized geocoded tweet word networks with entertainment television shows. Virtual reality software allows for the navigation of such complex networks at a scale that an individual traveler can comprehend and develop hypotheses for future testing.
The history of human communication network analysis, both representational and traffic based, reveals the strong values communication network analysts place on performing network analysis with large, real data-sets. Researchers study human communication networks ranging from nodes at the intrapersonal level through levels of increasing aggregation: individuals, groups, departments, organizations, nations, and cultures. They also map networks of words at various levels.
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