Kenneth Joseph

Two 1%s Don’t Make a Whole: Comparing Simultaneous Samples from Twitter’s Streaming API

We compare samples of tweets from the Twitter Streaming API constructed from different connections that tracked the same popular keywords at the same time. We find that on average, over 96% of the tweets seen in one sample are seen in all others. Those tweets found only in a subset of samples do not significantly differ from tweets found in all samples in terms of user popularity or tweet structure. We conclude they are likely the result of a technical artifact rather than any systematic bias.

On the Coevolution of Stereotype, Culture, and Social Relationships: An Agent-Based Model

The theory of constructuralism describes how shared knowledge, representative of cultural forms, develops between individuals through social interaction. Constructuralism argues that through interaction and individual learning, the social network (who interacts with whom) and the knowledge network (who knows what) coevolve. In the present work, we extend the theory of constructuralism and implement this extension in an agent-based model (ABM). Our work focuses on the theory’s inability to describe how people form and utilize stereotypes of higher order social structures, in particular observable social groups and society as a whole. In our ABM, we formalize this theoretical extension by creating agents that construct, adapt, and utilize social stereotypes of individuals, social groups, and society. We then use this model to carry out a virtual experiment that explores how ethnocentric stereotypes and the underlying distribution of culture in an artificial society interact to produce varying levels of social relationships across social groups. In general, we find that neither stereotypes nor the form of underlying cultural structures alone are sufficient to explain the extent of social relationships across social groups. Rather, we provide evidence that shared culture, social relations, and group stereotypes all intermingle to produce macrosocial structure.

The Fragility of Twitter Social Networks Against Suspended Users

Social media is rapidly becoming one of the mediums of choice for understanding the cultural pulse of a region; e.g., for identifying what the population is concerned with and what kind of help is needed in a crisis. To assess this cultural pulse it is critical to have an accurate assessment of who is saying what in social media. However, social media is also the home of malicious users engaged in disruptive, disingenuous, and potentially illegal activity. A range of users, both human and non-human, carry out such social cyber-attacks. We ask, to what extent does the presence or absence of such users influence our ability to assess the cultural pulse of a region? We conduct a series of experiments to analyze the fragility of social network assessments based on Twitter data by comparing changes in both the structural and content results when suspended users are left in and taken out. Because a Twitter account can be suspended for various reasons including spamming or spreading ideas that can lead to extremism or terrorism, we separately assess the impacts of removing apparent spam bots and apparent extremists. Experimental results demonstrate that Twitter-based network structures and content are unstable, and can be highly impacted by the removal of suspended users. Further, the results exhibit regional and temporal variation that may be related to the political situation or civil unrest. We also provides guidance on the differential impact of different types of potentially suspend-able users.

Check-ins in “Blau Space”: Applying Blau’s Macrosociological Theory to Foursquare Check-ins from New York City

Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social “parameters” (constraints). Actors that are closer together (more homogenous) in this social parameter space are more likely to interact. One of Blau’s most important hypotheses resulting from this work was that the consolidation of parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use Foursquare data from New York City to explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked in to by similar users. More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogenous across a set of four social constraints—racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that 14 (of 20) communities are statistically, and all but one qualitatively, homogenous along one of these social constraints, showing the relevance of Blau’s latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogenous on more than one parameter, represent socially isolated populations. We find communities homogenous on multiple parameters, including a homosexual community and a “hipster” community, that show support for Blau’s hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self-representation on social media.

Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets.

Exploring characteristics of suspended users and network stability on Twitter

Social media is rapidly becoming a medium of choice for understanding the cultural pulse of a region; e.g. for identifying what the population is concerned with and what kind of help is needed in a crisis. To assess this cultural pulse, it is critical to have an accurate assessment of who is saying what. Unfortunately, social media is also the home of users who engage in disruptive, disingenuous, and potentially illegal activity. A range of users, both human and non-human, carry out such social cyber-attacks. We ask, to what extent does the presence or absence of such users influence our ability to assess the cultural pulse of a region? Our prior research on this topic showed that Twitter-based network structures and content are unstable and can be highly impacted by the removal of suspended users. Because of this, statistical techniques can be established to differentiate potential types of suspended and non-suspended users. In this extended paper, we develop additional experiments to explore the spatial patterns of suspended users, and we further consider how these users affect structural and content concentrations via the development of new metrics and new analyses. We find significant evidence that suspended users exist on the periphery of social networks on Twitter and consequently that removing them has little impact on network structure. We also improve prior attempts to distinguish among different types of suspended users by using a much larger dataset. Finally, we conduct a temporal sentiment analysis to illustrate differences between suspended users and non-suspended users on this dimension.

An agent-based model for simultaneous phone and SMS traffic over time

The present work describes a utility-based, multi-agent, dynamic network model of phone call and SMS traffic in a population. The simulation is novel in its ability to generate interactions from both an asymmetric and a symmetric media simultaneously. Within the model, we develop and test a simple extension to the theory of media multiplexity, a well-known theory of how humans use the communication media available to them with different alters (friends). Model output qualitatively matches patterns in real data at the network-level and with respect to how humans use SMS and voice calls with different alters and thus shows general support for our theoretical claim.

Scalable computational techniques for centrality metrics on temporally detailed social network

Increasing proliferation of mobile and online social networking platforms have given us unprecedented opportunity to observe and study social interactions at a fine temporal scale. A collection of all such social interactions among a group of individuals (or agents) observed over an interval of time is referred to as a temporally-detailed (TD) social network. A TD social network opens up the opportunity to explore TD questions on the underlying social system, e.g., “How is the betweenness centrality of an individual changing with time?” To this end, related work has proposed temporal extensions of centrality metrics (e.g., betweenness and closeness). However, scalable computation of these metrics for long time-intervals is challenging. This is due to the non-stationary ranking of shortest paths (the underlying structure of betweenness and closeness) between a pair of nodes which violates the assumptions of classical dynamic programming based techniques. To this end, we propose a novel computational paradigm called epoch-point based techniques for addressing the non-stationarity challenge of TD social networks. Using the concept of epoch-points, we develop a novel algorithm for computing shortest path based centrality metric such as betweenness on a TD social network. We prove the correctness and completeness of our algorithm. Our experimental analysis shows that the proposed algorithm out performs the alternatives by a wide margin.

Contextual Sentiment Analysis

This study examines the role of context in evaluating responses to social media posts online. Current sentiment analysis tools evaluate the content of posts without considering the broader context that the post comes from. Utilizing data from an in-person study, we examine differences between perceived sentiment evaluation when social media response posts are viewed in isolation and perceived sentiment evaluation when social media responses are viewed in the context of the original post. We find that evaluations of responses viewed in context change over 50 % of the time. We validate this finding by utilizing simulated data to show the result is not simply a result of data manipulation or noisy data; furthermore, we explore results of this finding with current sentiment analysis tools, examining this result with subsets of our data with high and low kappa values.

A social-event based approach to sentiment analysis of identities and behaviors in text

ABSTRACT We describe a new methodology to infer sentiments held toward identities and behaviors from social events that we extract from a large corpus of newspaper text. Our approach draws on affect control theory, a mathematical model of how sentiment is encoded in social events and culturally shared views toward identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a three-dimensional sentiment “profile” for each concept. We can also infer when multiple sentiment profiles for a concept are likely to exist. We provide a case study of a large newspaper corpus on the Arab Spring, which helps to validate our approach.