Daniel A. McFarland

Dynamic network visualization

Increased interest in longitudinal social networks and the recognition that visualization fosters theoretical insight create a need for dynamic network visualizations, or network “movies.” This article confronts theoretical questions surrounding the temporal representations of social networks and technical questions about how best to link network change to changes in the graphical representation. The authors divide network movies into (1) static flip books, where node position remains constant but edges cumulate over time, and (2) dynamic movies, where nodes move as a function of changes in relations. Flip books are particularly useful in contexts where relations are sparse. For more connected networks, movies are often more appropriate. Three empirical examples demonstrate the advantages of different movie styles. A new software program for creating network movies is discussed in the appendix.

Bowling Young: How Youth Voluntary Associations Influence Adult Political Participation

Do the voluntary activities of youth increase political engagement in adulthood? Political participation is typically characterized by inertia: reproduced within families, highly correlated with social class, and largely stable after the onset of adulthood. This research illustrates an element of political socialization that occurs just before the transition into full citizenship, that mimics adult civic life, and that can be available regardless of family advantage. The authors use two longitudinal national datasets to identify the kinds of voluntary associations that encourage members to be more politically active later in life. They find that general involvement in extracurricular activities is important, but that in particular, involvement in youth voluntary associations concerning community service, representation, speaking in public forums, and generating a communal identity most encourage future political participation. The authors find these effects net of self-selection and causal factors traditionally characterized in political socialization research. The influence of youth voluntary associations on future political activity is nontrivial and has implications for both democratic education and election outcomes.

Student Resistance: How the Formal and Informal Organization of Classrooms Facilitate Everyday Forms of Student Defiance1

Critical and resistance theorists propose that race and class backgrounds influence everyday forms of student resistance in schools. This article argues that the microsocial process of student defiance is less characterized by individual traits of race and class than by the formal and informal organizational characteristics of social settings. Using unique data on resistance in multiple schools and classrooms, this article finds that defiant behaviors arise when instructional formats give students access to public discourse and when students have advantaged social network relations. Social opportunities of tasks, coupled with political opportunities of networks, enable students to consistently undermine and redirect classroom affairs. The results suggest that resistant behavior is more the result of organizational features of social networks and instruction than “alienation” factors, and is therefore rectifiable through classroom management.

Ties That Last Tie Formation and Persistence in Research Collaborations over Time

Using a longitudinal dataset of research collaborations over 15 years at Stanford University, we build a theory of intraorganizational task relationships that distinguishes the different factors associated with the formation and persistence of network ties. We highlight six factors: shared organizational foci, shared traits and interests, tie advantages from popularity, tie reinforcement from third parties, tie strength and multiplexity, and the instrumental returns from the products of ties. Findings suggest that ties form when unfamiliar people identify desirable and matching traits in potential partners. By contrast, ties persist when familiar people reflect on the quality of their relationship and shared experiences. The former calls for shallow, short-term strategies for assessing a broad array of potential ties; the latter calls for long-term strategies and substantive assessments of a relationship’s worth so as to draw extended rewards from the association. This suggests that organizational activities geared toward sustaining persistent intraorganizational task relationships need to be different from activities aimed at forging new ones.

Motives and Contexts of Identity Change: A Case for Network Effects

In this paper we interrelate different theories of identity and describe how various social contexts and cognitive motives influence the process of identity change. We consider two competing theories about the linkage of contexts with motives for identity change: the effect of category traits, based on social identity theory, and the effect of social networks, based on identity theory. To explore these relations, we use data collected on more than 6,000 adolescents at six high schools in two consecutive school years. Multilevel logit models reveal a strong relationship between contexts and perceived identity imbalances, and a strong effect of identity imbalance on identity change. More important than category traits are the social network characteristics of prominence, homogeneity, and bridging; these form social contexts that affect perceptions of identity imbalance, and the perceptions in turn lead to a heightened incidence of identity change.

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measures calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.

Citing for high impact

The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publications that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that idiosyncratic citation patterns are characteristic for low impact papers; while narrow, discipline-focused citation patterns are common for medium impact papers. Our results show that crossing-community, or bridging citation patters are high risk and high reward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.

Network Ecology and Adolescent Social Structure

Adolescent societies—whether arising from weak, short-term classroom friendships or from close, long-term friendships—exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation

Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing. We describe a system for detecting elements of interactional style: whether a speaker is awkward, friendly, or flirtatious. We create and use a new spoken corpus of 991 4-minute speed-dates. Participants rated their interlocutors for these elements of style. Using rich dialogue, lexical, and prosodic features, we are able to detect flirtatious, awkward, and friendly styles in noisy natural conversational data with up to 75% accuracy, compared to a 50% baseline. We describe simple ways to extract relatively rich dialogue features, and analyze which features performed similarly for men and women and which were gender-specific.

It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates

Automatically detecting human social intentions from spoken conversation is an important task for dialogue understanding. Since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended social meaning. We investigate this difference between intention and perception by using a spoken corpus of speed-dates in which both the speaker and the listener rated the speaker on flirtatiousness. Our flirtation-detection system uses prosodic, dialogue, and lexical features to detect a speakers intent to flirt with up to 71.5% accuracy, significantly outperforming the baseline, but also outperforming the human inter-locuters. Our system addresses lexical feature sparsity given the small amount of training data by using an autoencoder network to map sparse lexical feature vectors into 30 compressed features. Our analysis shows that humans are very poor perceivers of intended flirtatiousness, instead often projecting their own intended behavior onto their interlocutors.