Searching for Product Experience Attributes in Online Information Sources

Using the Web, consumers not only find product characteristics from manufacturers and sellers; they can also exchange opinions with other third parties. Learning about such “experience attributes” builds confidence in purchasing decisions and establishes trust between parties in transactions. However, little is known about the search process for these experience attributes. In the current study, 65 participants researched a product, reporting its characteristics as well as salient experience attributes. By analyzing their search diaries and questionnaire responses, patterns for finding product characteristics versus experience attributes were compared. Precision on task was the same, however, product characteristics searches were likely to originate at search engines. Seller-dominated sites were favored over independent sites in searches for product characteristics, while the opposite was true for experience attributes. Finally, searches for experience attributes were not as broad or deep as those for product characteristics, suggesting that consumers focus their attention on fewer sources

Learning the Lingo? Gender, Prestige and Linguistic Adaptation in Review Communities

Women and men communicate differently in both face-to- face and computer-mediated environments. We study linguistic patterns considered gendered in reviews contributed to the Internet Movie Database. IMDb has been described as a male-majority community, in which females contribute fewer reviews and enjoy less prestige than males. Analyzing reviews posted by prolific males and females, we hypothesize that females adjust their communication styles to be in sync with their male counterparts. We find evidence that while certain characteristics of “female language” persevere over time (e.g., frequent use of pronouns) others (e.g., hedging) decrease with time. Surprisingly, we also find that males often increase their use of “female” features. Our results indicate, that even when they resemble men’s reviews linguistically, women’s reviews still enjoy less prestige and smaller audiences

What's Congress Doing on Twitter?

As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how officials use these communication tools. Using data from 380 members of Congress’ Twitter activity during the winter of 2012, we find that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials’ social media behavior

Tweeting Vertically? Elected Officials’ Interactions with Citizens on Twitter

Enthusiasts propose that social media promotes vertical political communication, giving citizens the opportunity to interact directly with their representatives. However, skeptics claim that politicians avoid direct engagement with constituents, using technology to present a façade of interactivity instead. This study explores if and how elected officials in three regions of the world are using Twitter to interact with the public. We examine the Twitter activity of 15 officials over a period of six months. We show that in addition to the structural features of Twitter that are designed to promote interaction, officials rely on language to foster or to avoid engagement. It also provides yet more evidence that the existence of interactive features does not guarantee interactivity

Chicago Politicians on Twitter

This paper uses data from 1,042 tweets posted by or mentioning Chicago Aldermen or Mayor Rahm Emanuel to examine how Chicago politicians use social media. Twitter provides a public communication medium in which constituents and their representatives can have two-way conversations that others can witness and record, and we used qualitative and social network methods to examine conversations between Chicagoans and representatives in city government. We coded the contents of each tweet over the two-week time period (e.g., official business, fundraising) and created representations of the social networks created by the users’ following behaviors. These networks indicate who receives politicians’ tweets and help identify the audiences for political messaging in social media. Our analysis indicates that Chicago’s Aldermen and Mayor use Twitter for social conversations more often than political ones, and that only a small number of Aldermen dominate the resulting conversation networks.Sponsorship: Social Network Research Group at IIT, IIT Graduate Colleg

Doing What I Say: Connecting Congressional Social Media Behavior and Congressional Voting

Public officials’ communication has been explored at length in terms of how such their statements are conveyed in the traditional media, but minimal research has been done to examine their communication via social media. This paper explores the kinds of statements U.S. officials are making on Twitter in terms of the actions they are trying to achieve. We then analyze the correlation between these statements, Congressional communication network structures, and voting behavior. Our analysis leverages over 29,000 tweets by members of Congress in conjunction with existing DW-NOMINATE voting behavior data. We find that pro-social and self-promoting statements correlate with Congressional voting records but that position within the Congressional communication network does not correlate with voting behavior.Sponsorship: Social Networks Research Group at IIT, IIT Graduate Colleg

Social and Linguistic Behavior and its Correlation to Trait Empathy

A growing body of research exploits social media behaviors to gauge psychological character- istics, though trait empathy has received little attention. Because of its intimate link to the abil- ity to relate to others, our research aims to predict participants’ levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari- ance explained in Davis’ Interpersonal Reactivity Index (IRI) scores on four constructs (em- pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy’s role in facilitating interactions on social media

Show Me You Care: Trait Empathy, Linguistic Style and Mimicry on Facebook

Linguistic mimicry, the adoption of another’s language patterns, is a subconscious behavior with pro-social benefits. However, some professions advocate its conscious use in empathic communication. This involves mutual mimicry; effective communicators mimic their interlocutors, who also mimic them back. Since mimicry has often been studied in face-to-face contexts, we ask whether individuals with empathic dis- positions have unique communication styles and/or elicit mimicry in mediated communication on Facebook. Participants completed Davis’ Interpersonal Reactivity Index and provided access to Facebook activity. We confirm that dispositional empathy is correlated to the use of particular stylistic features. In addition, we identify four empathy profiles and find correlations to writing style. When a linguistic feature is used, this often “triggers” use by friends. However, the presence of particular features, rather than participant dispo- sition, best predicts mimicry. This suggests that machine-human communications could be enhanced based on recently used features, without extensive user profiling

Investigating User Perception of Gender Bias in Image Search

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results