Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks

Research on group cohesion often relies on individual perceptions, which may not reflect the actual social structure of groups. This study draws on social network theory to examine the relationship between observable structural group characteristics and individual perceptions of group cohesion. Leveraging Facebook data, we extracted and partitioned the social networks of 109 participants into groups using a modularity algorithm. We then surveyed perceptions of cohesion, and computed group density and size using social network analysis. Out of six linear mixed effects models specified, a random intercept and fixed slope model with group size as a predictor of perceived group cohesion emerged as best fitting. Whereas group density was not linked to perceived cohesion, size had a small negative effect on perceived cohesion, suggesting that people perceive smaller groups as more cohesive. We discuss the potential of social network analysis, visualization tools, and Facebook data for advancing research on groups.     

戈公振《世界报业考察记》校读记

商务印书馆2017年整理版戈公振遗著《世界报业考察记》，嘉惠学林甚大，然其中有少量文字讹误，对此略做校正与辨析。     

基于关键词分析我国图书馆焦虑研究态势

国内关于图书馆焦虑的研究已经开展多年，并取得一定的研究成果。以我国图书馆焦虑的研究文献的关键词为统计对象，基于共词分析，利用聚类方法和战略坐标图分析我国在该领域的研究现状。结果表明，目前该领域的研究集中于读者自身、图书馆环境和图书馆员三大因素的研究，从理论上研究图书馆焦虑起因的关注度在降低，图书馆焦虑缓解措施的可行性和有效性研究比较缺乏。最后给出了该领域在下一步研究中的可能方向，供研究人员参考。     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

A Simple Toxicokinetic Model Exhibiting Complex Dynamics and Nonlinear Exposure Response

Uncertainty in model predictions of exposure response at low exposures is a problem for risk assessment. A particular interest is the internal concentration of an agent in biological systems as a function of external exposure concentrations. Physiologically based pharmacokinetic (PBPK) models permit estimation of internal exposure concentrations in target tissues but most assume that model parameters are either fixed or instantaneously dose-dependent. Taking into account response times for biological regulatory mechanisms introduces new dynamic behaviors that have implications for low-dose exposure response in chronic exposure. A simple one-compartment simulation model is described in which internal concentrations summed over time exhibit significant nonlinearity and nonmonotonicity in relation to external concentrations due to delayed up- or downregulation of a metabolic pathway. These behaviors could be the mechanistic basis for homeostasis and for some apparent hormetic effects.     

How publics react to situational and renewing organizational responses across crises: Examining SCCT and DOR in social-mediated crises

The prevalence of social media among networked publics calls for more research regarding how organizations can conduct effective crisis communication on social networking sites. Based on the situational crisis communication theory (SCCT) and the discourse of renewal (DOR) theory, this study examined how social media publics’ sentiments were affected by situational and renewing organizational responses in various clusters of crises. Twitter data of six crises representing three crisis clusters varying in the responsibility attribution (i.e., ambiguous, accidental, and preventable) were collected. We conducted a content analysis on organizations’ official tweets during crises (N = 59) and sentiment analysis on publics’ replies on Twitter (N = 4,340). The results showed that publics’ positive sentiments toward organizations were affected by organizational crisis responses that included instructing information, sympathy, systemic organizational learning, and effective organizational rhetoric. We recommend that crisis managers express sympathy toward publics as well as organizational learning that prevents a crisis from happening again.     

The coevolution of trade agreements and investment treaties: Some evidence from network analysis

Regional trade agreements (RTAs) and bilateral investment treaties (BITs) are expected to promote trade and investment relationships. One critical feature of such agreements is the network, so the multiplex coevolution of RTAs and BITs should be captured by the dynamics of their two networks. Although many studies have examined the roles of RTAs and BITs, most studies do not account for crucial network properties. This study explores how RTAs and BITs coevolve by applying a stochastic actor-oriented model of multiplex network evolution. In particular, we examine the roles of (i) cross-network dyadic interinfluences and (ii) within- and cross-network preferential attachments to discuss the dynamic relationships between RTAs and BITs. The results are as follows. First, our estimation supports cross-network dyadic interinfluences. Countries that sign a BIT are willing to establish an RTA, while those that sign an RTA are reluctant to establish a BIT. Second, concerning preferential attachments, countries prefer to sign BITs with partners that have more RTA and BIT links. However, countries tend to form RTAs with partners that have more BIT links but are reluctant to form RTAs with those that have more RTA links. We discuss possible justifications for these results, including arguments regarding the benefits and costs associated with the formation of RTAs and BITs.     

The Solidary Relationship’s Consequences for the Ebb and Flow of Activism: Collaborative Evidence from Life-History Interviews and Social Media Event Analysis,

Acting in solidarity with deprived others has become a central topic in social movement research. The explanations of solidarity activism or political altruism are few. However, social movement researchers have claimed that solidarity with out-of-group others is a by-product of in-group interaction. In contrast, we argue that out-group interaction with the deprived other and the formation of a solidary relationship is central to the ebb and flow of solidarity activism. We investigate the Danish refugee solidarity movement and show that the meeting with the deprived other 1) brings about an interaction order which makes an ethical demand on the activists to care for the other both within the bounds of the situations and in the future; 2) enacts and amplifies activists’ values and beliefs because the deprived other becomes an exemplar of the injustice and the need to help the broader group of people in the same fragile situation. We develop and test this theory drawing on 42 life-history interviews and a social media dataset containing a panel of 87,455 activists participating in refugee solidarity groups.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

The problem of intersectionality as an approach to digital activism: the Women’s March on Washington’s attempt to unite all women

ABSTRACT

This study examined the convergence of activism and intersectionality to understand how communicators create messages about social justice issues using social media. This is particularly relevant for public relations today, as digital activism almost ubiquitously involves bringing together conflicting publics who are active and social media-savvy, meanwhile maintaining an organizational brand/mission. Using the 2017 Women’s March on Washington (WMW) as an object of study, we explored how campaign messages reflected principles of intersectionality, consensus- and dissensus-based communication, and organizational self-reflection. We conducted a thematic analysis of posts from the WMW’s social media accounts as well as media quotes by the organizational leaders to get at the leaders’ intentions in their message design. Data suggested that messages of inclusivity as well as of necessary discord were employed to enact political change for WMW’s publics. We argue that although the WMW was not wholly intersectional, particularly in determining its political agenda, the efforts toward intersectionality are notable for theory-building and reflective practice, particularly for social mediated campaigns. The study proposes a theory for digital intersectional communication to guide future research and advocacy work.