Gender and Power: How Gender and Gender Environment Affect Manifestations of Power

Prabhakaran, Vinodkumar; Reid, Emily E.; Rambow, Owen

doi:10.3115/v1/d14-1211

Cited by 26 publications

(11 citation statements)

References 17 publications

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“…Prabhakaran et al (2012Prabhakaran et al ( , 2014b used indicators of overt displays of power (ODPs) that place constraints on the recipient (e.g., "come to my office now") to identify power dynamics within the corporate email communications of top Enron executives. Indicators were obtained using a supervised model trained on manual annotations and revealed that male superiors used significantly more ODPs compared to male and female subordinates and that female superiors used the least ODPs of all.…”

Section: Social Relationships Through the Process Of Communicationmentioning

confidence: 99%

“…Another approach to power measurement uses message control. Prabhakaran et al (2014b) analyzed US presidential primary debates to identify speaker turns when conversation changed from one topic to another. Higher-powered candidates-those posting higher poll numbers-were less likely to shift topics during the course of the debate, suggesting that control of the conversation is a consequence of electoral popularity.…”

Section: Social Relationships Through the Process Of Communicationmentioning

confidence: 99%

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Machine Translation: Mining Text for Social Theory

2016

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More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight. 18.1

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Section: Social Relationships Through the Process Of Communicationmentioning

confidence: 99%

Section: Social Relationships Through the Process Of Communicationmentioning

confidence: 99%

Machine Translation: Mining Text for Social Theory

2016

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“…Even for "conventional" digital documents, we know, at least in the UK civil service, registries and file plans have all but vanished (Allan, 2014(Allan, , 2015. The Enron emails that were made available during the legal investigations into the business consisted of 620,000 assorted emails and the only way that sense could be made of them was by using advanced computational and statistical techniques at the Language Technology Institute at Carnegie Mellon University (Klimt and Yang, 2004b) and in the Department of Computing Science at Columbia University (Prabhakaran et al, 2014). Such techniques are now becoming common in the digital forensics community (in commercial tools such as Nuix) 1 Their potential for application to the archival material would arguably only be a sophisticated extension in the digital world to the fundamental concept of cataloging (TNA, 2016).…”

Section: The Challenge Of the Newmentioning

confidence: 99%

Artificial Fibers—The Implications of the Digital for Archival Access

Moss

Thomas

Gollins

2018

Front. Digit. Humanit.

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This article explores how current methods and approaches in archives are under serious challenge because of the changes brought about by the move to the digital. The availability of digital records has meant that new needs and new possibilities have opened up for users, including new ways of reading. The nature of archives themselves are changing-they are moving from being collections of individual texts to be pored over to data to be made sense of. New tools and techniques have emerged and are available now which offer radical new possibilities for research, but these bring new challenges about trust and the sheer volume of records to be handled. The traditional approaches of applying metadata to facilitate the finding of relevant material and of regarding digital documents as something like electronic paper is no long viable. What is needed is a new approach in which archivists and scholarly researchers see archives as collections of data which are capable of analysis by a range of sophisticated tools and which are capable of being interpreted in a range of different ways.

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“…A closely related area of research has been predicting power relations in dialog Danescu-Niculescu-Mizil et al, 2012;Strzalkowski et al, 2013;Prabhakaran et al, 2014b;Prabhakaran and Rambow, 2013]. This can include several types of power relationships, such as hierarchical and administrative power, as well as influence.…”

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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Gender and Power: How Gender and Gender Environment Affect Manifestations of Power

Cited by 26 publications

References 17 publications

Machine Translation: Mining Text for Social Theory

Machine Translation: Mining Text for Social Theory

Artificial Fibers—The Implications of the Digital for Archival Access

Detecting influencers in social media discussions

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