Modeling topic control to detect influence in conversations using nonparametric topic models

Nguyen, Viet-An; Boyd-Graber, Jordan; Resnik, Philip; Cai, Deborah A.; Midberry, Jennifer; Wang, Yuanxin

doi:10.1007/s10994-013-5417-9

Cited by 36 publications

(16 citation statements)

References 67 publications

(86 reference statements)

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“…Machine learning algorithms now understand the flow and ebb of conversations, e.g. detecting who are the powerful individuals in control of conversations (Nguyen et al, 2014). Classification algorithms can group employees, customers, projects, tasks, and even meetings into distinct buckets to allocate managerial attention and resources.…”

Section: Open-ended Big Data Systems and Computer Augmented Transparencymentioning

confidence: 99%

Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency

Schildt

2016

Innovation

117

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Big data and sophisticated algorithms enable software to handle increasingly complex tasks, such as detecting fraud, optimizing logistics routes, and even driving cars. Beyond technical tasks, algorithms enable new ways to organize work. In this article, I suggest a distinction of optimizing-oriented and open-ended systems leveraging big data and examine how they are shaping organizational design. The optimizing-oriented systems, typically based on numerical data, enable smarter control of well-defined tasks, including algorithmic management of human work. Open-ended systems, often based on textual data or visualizations, can provide answers to a broad range of managerial questions relevant to effective organizing, thereby enabling smarter and more responsive definition of tasks and allocation of resources and effort. Algorithms processing conversations that naturally take place in organizations can form 'computer augmented transparency' , creating a host of potential benefits, but also threats. These developments are leading to a wave of innovation in organizational design and changes to institutionalized norms of the workplace.

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Section: Open-ended Big Data Systems and Computer Augmented Transparencymentioning

confidence: 99%

Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency

Schildt

2016

Innovation

117

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“…Although many factors may shape these interactions, an officer's words are undoubtedly critical: Through them, the officer can communicate respect and understanding of a citizen's perspective, or contempt and disregard for their voice. Furthermore, the language of those in positions of institutional power (police officers, judges, work superiors) has greater influence over the course of the interaction than the language used by those with less power (12)(13)(14)(15)(16). Measuring officer language thus provides a quantitative lens on one key aspect of the quality or tone of police-community interactions, and offers new opportunities for advancing police training.…”

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confidence: 99%

Language from police body camera footage shows racial disparities in officer respect

Voigt

Camp

Prabhakaran

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

304

185

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Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and the building of police-community trust.racial disparities | natural language processing | procedural justice | traffic stops | policing

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“…In the future we would like to explore adding additional components and features. Particularly, features related to topic have been found to be useful in detecting influencers and power [Nguyen et al, 2013b;. We opted to exclude it thus far since most of our discussions tend to be on a single topic.…”

Section: Resultsmentioning

confidence: 99%

“…In computational linguistics, several authors have detected influencers in a single conversation using the actual discussion [Quercia et al, 2011;Nguyen et al, 2013b;Young et al, 2011;Prabhakaran and Rambow, 2013]. This work has explored detecting influencers using features such as dialog structure, persuasion, and topic control.…”

Section: Influence In Social Networkmentioning

confidence: 99%

Detecting influencers in social media discussions

Rosenthal

2014

XRDS

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In the past decade we have been privileged to witness the creation and revolution of social media on the World Wide Web. The abundance of content available on the web allows us to analyze the way people interact and the roles they play in a conversation on a large scale. One such role is influencer in the conversation. Detecting influence can be useful for successful advertisement strategies, detecting terrorist leaders and political campaigning. We explore influence in discussion forums, weblogs, and micro-blogs using several components that have been found to be indicators of influence. Our components are author traits, agreement, claims, argumentation, persuasion, credibility, and certain dialog patterns. In the first portion of this thesis we describe each of our system components. Each of these components is motivated by social science through Robert Cialdini's "Weapons of Influence" [Cialdini, 2007]. The weapons of influence are Reciprocation, Commitment and Consistency, Social Proof, Liking, Authority, and Scarcity. We then show the method and experiments for classifying each component. In the second part of this thesis we classify influencers across five online genres and analyze which features are most indicative of influencers in each genre. The online genres we explore are Wikipedia Talk Pages, LiveJournal weblogs, Political Forum discussions, Create Debate debate discussions, and Twitter microblog conversations. First, we describe a rich suite of features that were generated using each of the system components. Then, we describe our experiments and results including using domain adaptation to exploit the data from multiple online genres. Finally, we also provide a detailed analysis of a single weapon of influence, social proof, and its impact in detecting influence in Wikipedia Talk Pages. This provides a single example of the usefulness of providing comprehensive components in the detection of influence. The contributions of this thesis include a system for predicting who the influencers are in online discussion forums. We provide an evaluation of a rich set of features inspired by social science. In our system, each feature set used to detect influence is complex and computed by a system component. This allows us to provide a detailed analysis as to why the person was chosen as an influencer. We also provide a comparison of differences across several online discussion datasets and exploit the differences across the different genres to provide further improvements in influence detection.

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Modeling topic control to detect influence in conversations using nonparametric topic models

Cited by 36 publications

References 67 publications

Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency

Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency

Language from police body camera footage shows racial disparities in officer respect

Detecting influencers in social media discussions

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