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Here are some SNA approaches to media studies:
Abstract. To test hypotheses about presidential cabinet network centrality and presidential job approval over time and to illustrate automatic social network identification, this research mined the social networks among cabinets of Presidents Reagan through G.W. Bush based on the members’ co-occurrence in news stories. Each administration’s data was sliced into time intervals corresponding to Gallup presidential approval polls. It was hypothesized that when the centrality of the president is lower than that of other cabinet members, job approval ratings are higher. This is based on the assumption that news is generally negative and when the president stands above the other cabinet members in network centrality, he or she is more likely to be associated with the negative press coverage in the minds of members of the public. The hypothesis was supported. Nevertheless, when the positive and negative sentiment of news stories is added, each administration was found to have different effects.
To test hypotheses concerning presidential cabinet network centrality and its relationship to presidential job approval, this research mined the social network structure of President Barack Obama’s cabinet members through automatic network analysis of all New York Times and Washington Post stories including any of 24 individual cabinet members and the president. The WORDij semantic-network software identified their co-occurrences in these news stories. The software also segmented the aggregate text into two-week intervals based on the average time between Gallup presidential approval polls. Time-series analysis linked network centrality with presidential job approval after removing serial autocorrelation. The research tested the hypothesis that when the centrality of the president is higher than the centrality of the cabinet members, presidential job approval ratings are lower. Conversely, when the centrality of the president is lower relative to average cabinet member centrality, job approval ratings are higher. This is based on the reasoning that press coverage is commonly negative, and the president absorbs more negative press sentiment when he stands higher in centrality than the other cabinet members, acting as a metaphoric “lightening rod.” When the president’s centrality is lower relative to the cabinet average, this lightening rod effect dissipates as other cabinet members would absorb more of the negative press content as publics process information about the administration. The hypothesis was supported for the Obama administration and his cabinet with a significant negative correlation between relative presidential centrality and job approval at a time lag of l=3, a period of 6 weeks.
Unprecedented social and technological developments call into question the meanings and boundaries of privacy in contemporary China. This study examines the discourse of privacy on Sina Weibo, the country’s largest social medium, by performing a semantic network analysis of 18,000 postings containing the word “隐私 (privacy).” The cluster analysis identifies eleven distinct yet organically related concept clusters, each representing a unique dimension of meaning of the complex concept. The interpretation of the findings is situated in the discussion of the rapidly evolving private realm in relation to emerging new contexts of the public realm. Privacy, justified for both its instrumental functions and intrinsic values, both reflects and constitutes new forms of sociality on the socio-techno space of Weibo.
This paper studies the network structure, conversation patterns, and semantic content of socially mediated exchanges via the microblogging service Twitter. The project tracked interactions and messages beginning with a randomly selected user, followed by a snowball sample of the user’s network. From 01/24/2010 to 04/24/2010 a total of 5776 unique users were collected, with a total of 3,392,138 tweets. The content of tweets was then examined for two groups: users with a highly centralized (radial) network, and those with a densely interlocked network. The most central English language word pairs were identified through semantic software analysis using the WORDij package, and also subjected to language analysis using the LIWC2007 software. Degree of interactivity between ego and alters was also measured. The findings suggest a relationship between network structure, interaction patterns, and message content that may be described as producing distinct genres of Twitter experience: one practiced by conversationalists, who focus on information sharing and strategic identity construction, and another by protagonists, who are more inwardly focused and performative, with minimal interaction. These genres have implications for scholars interested in the interdependency between technology and human communication behaviors.
ABSTRACT The literature argues that the amount of media coverage of issues sets the public agenda. What sets the media agenda appears to largely be governmental communication. It was hypothesized that governmental actors would attract media to the issue of acid rain. Once coverage started, interest groups would push the media bandwagon for wider sectors of society. This creates a climate of “buzz journalism.” After conflict among interest groups accelerated and the wagon reached optimal speed, governmental actors would brake the issue cycle and media coverage would dissipate. It was further hypothesized that a period of “long-tail journalism” would ensue in which there was little media coverage over the years without this governmental-generated buzz of coverage. The plot of the number of stories per year for the next 25 years showed that there was some coverage but very predictable in its residual factual nature with no hooks to policy considerations. Not until again in 2009 was acid rain connected with a governmental initiative, the “Cap and Trade ”policy” deliberations set in motion by the Obama administration. Coverage of acid rain moved again above the long-tail, although only slightly with weakly organized semantic networks. To test these hypotheses, coverage of acid rain during 1977—1989 in the New York Times Index (459 abstracts) and the Congressional Quarterly Almanac (43 articles) were content analyzed to identify co—appearing actors (n=354). These sources were used because electronic access to relevant news documents as not robust. Actor network data were aggregated by year and actor centrality measured, serving as independent variables in time—series regressions with media coverage as the dependent variable.
The hypotheses were supported. Governmental actors had an elliptical power curve. They appeared to start and stop the media top, but interests groups put the spin on in the middle. Spin, however, was not the only cycle apparent. This suggests a more comprehensive social cleansing model of media agenda setting. What is left after government is done with an issue is the residual core fabric of the content factual content.
Abstract. The current research compares celebrities to public intellectuals, dead or alive, in their amounts of traditional and new media coverage and in their agency in online social discourse networks. Previous research studied only public intellectuals on these variables. A comprehensive theory is presented that brings together media-related processes about celebrities and public intellectuals into a coherent framework and expands the theory to also include online discussion content variables. WordLink software is used to index semantic-network structures, with additional content variables examined. Eleven hypotheses are derived from the broadly-based theory. The hypotheses are supported by the data. Highlights include: discussion content about celebrities is more entropic, more focused on peripheral content, more socio-emotional, and the discussants more narcissistic. They also focus on less abstract concepts than do those discussing public intellectuals. Although celebrities receive more media coverage than public intellectuals, the latter have twice the size of online social networks associated with them. The glow of celebrities produced and managed as the output of mega-media organizations fades relatively quickly after their death, while the more focused conceptual beams of public intellectuals, even after their corporeal passing, are associated with activation of more developed social networks in the time-suspension of cyberspace.
Abstract: Social media are fundamentally based on communication networks containing friends, acquaintances, or others. Because communication is essential to collaboration on activities, we assume that those who have more contacts in work-oriented social media are more likely to be collaborative in their work behaviors. Design educators have been stressing of importance of collaboration in training recent cohorts of design students. Our goal was to empirically examine designers on the LinkedIn social media site in terms of how they describe collaboration and why. The results supported hypotheses that designers with larger networks of online contacts were more verbal, had higher collaborative word use, were more positive, were more evaluative, used more competence-oriented words, and had semantic networks for collaboration that were more complex, with greater discrimination, differentiation, and integration. Given the experiences of current young adults with social media, we further hypothesize that future design work will become increasingly collaborative.
We investigated possible causal relationships between a professional association’s division network structure based on co-memberships, and the division network structure based on the semantic similarity of papers presented at annual meetings. Data from the International Communication Association (ICA), a basic-research focused organization of academic social scientists with 21 divisions, provide for an analysis at two points in time, 2007 and 2011. QAP correlations among the four networks entered into a quasi-experimental cross-lagged correlation design suggested evidence for possible causality. Compared to the no cause baseline, the time 1 co-membership network structure was a significant predictor of the time 2 semantic division network structure. The reverse relationship was not significant. As well, there is considerable reduction of the size of the synchronous correlation of the semantic and co-membership division networks from time 1 to time 2. Noteworthy was also the pattern of diachronic association of the same kind of network. The semantic division network at time 1 explained only 31% of the same network at time 2. Likewise, the co-membership network at time 1 explained only 25% of that network at time 2. This would be consistent with the basic research focus of the association. Such a focus privileges novelty. The paper uses theory to form the research questions and interpretations. Because of the limitations of the statistical model, and the case study design, these results should be taken as exploratory and suggestive. Future research may reduce these limitations.
Abstract—This research demonstrated a new approach to time-series analysis of semantic network data. Three years of the Obama administrations coverage in the New York Times and Washington Post was extracted from Lexis-Nexis. The text was sliced into 74 two-week time intervals corresponding to frequency of Gallup polls. Words appearing in three word positions on either side of each word were tabulated, one for the aggregate text file and 74 times for each of the time slice data. A matrix of word pairs by time slices created the basis for computing a time slice similarity score for each pair of slices. These time units then became nodes in a network analysis with link strengths defined by semantic similarity. Network analysis procedures were applied to explore the meaning of the structures identified.While events can be laid upon a chronological time series, events and event frames may often be of a different character than merely time-dependent. Some events/frames operate in spurts, perhaps with considerable time between them that may result in the analyst missing the wholeness of the event/frames. Some events/frames retrench and add new elements as they “move back in time, then forward again.” Some events/frames contain arcs in which an alternative framing is offered by opposing groups. Some events/frames are pivotal, changing the fundamental dynamics of the overall event/frame space. Some events mark epics that may begin after a pivotal time.
Depending on socio-political perspectives some events/frames may be treated from a common semantic domain, while the same such events/frames may from a different socio-political be treated quite differently. Methods that foster parsimony in treating events/frames may be preferred in some circumstances.
The method used here, analyzing time as nodes in a network based on their similarity of word pair frequencies appears to offer the most flexibility for observing these various kinds of events and frames. Turning time outside in, that is treating time slices as nodes and their semantic similarities as links, yields some unique value. The event/time space is analyzed for its underlying network structure. Community detection reveals a simplified structure. Particular time nodes serve specialized roles in the network. Exploration with other networks can elaborate a conceptual framework for event/time space.
Abstract—Analysis of sentiment expressed in political systems’ communication over long periods of time has been difficult. This research illustrates a method based on network analysis, the Sentiment Network Analyzer (SNAZ). It identifies weighted shortest paths between seed words and 3,500 target sentiment words as these occur in semantic networks extracted from open-source documents sliced into time intervals. Computing the normalized intensity ratios of positive and negative sentiment for each time slice enables application of the “Losada Line.” For a system to be flourishing there must be at least 2.9 times more positive than negative communication. Below that ratio the system is languishing. Excessive positivity above a ratio of 11.6 marks the disintegration of a system into chaotic oscillation. We collected and analyzed five years of documents propaganda mentioning the Taleban from Afghani and Pakistani sources transcribed by BBC International Monitoring. Likewise, we extract and analyze stories communicated by Radio Free Europe/Radio Liberty (RFE/RL) connected with Afghanistan over the same five-year period. Semantic network and sentiment network analysis is coupled with the computation of positivity ratios in each time slice during this period. Taleban content is generally evident of flourishing, except for a period of oscillation between flourishing and excessive positivity beginning in the third quarter of 2010. RFE/RL is consistently languishing, reaching the 2.9 flourishing level in only one period. We discuss possible reasons. We also consider some implications for perception management and counterterrorism strategy.