Jordan Carpenter

When to Use Your Head and When to Use Your Heart The Differential Value of Perspective-Taking Versus Empathy in Competitive Interactions

Four studies explored whether perspective-taking and empathy would be differentially effective in mixed-motive competitions depending on whether the critical skills for success were more cognitively or emotionally based. Study 1 demonstrated that individual differences in perspective-taking, but not empathy, predicted increased distributive and integrative performance in a multiple-round war game that required a clear understanding of an opponent’s strategic intentions. Conversely, both measures and manipulations of empathy proved more advantageous than perspective-taking in a relationship-based coalition game that required identifying the strength of interpersonal connections (Studies 2-3). Study 4 established a key process: perspective-takers were more accurate in cognitive understanding of others, whereas empathy produced stronger accuracy in emotional understanding. Perspective-taking and empathy were each useful but in different types of competitive, mixed-motive situations—their success depended on the task–competency match. These results demonstrate when to use your head versus your heart to achieve the best outcomes for oneself.

Analysing Biases in Human Perception of User Age and Gender from Text

User traits disclosed through written text, such as age and gender, can be used to personalize applications such as recommender systems or conversational agents. However, human perception of these traits is not perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing experiment on guessing age and gender from tweets. We systematically analyze the quality and possible biases of these predictions. We identify the textual cues which lead to miss-assessments of traits or make annotators more or less confident in their choice. Our study demonstrates that differences between real and perceived traits are noteworthy and elucidates inaccurately used stereotypes in human perception.

Studying Personality through the Content of Posted and Liked Images on Twitter

Interacting with images through social media has become widespread due to ubiquitous Internet access and multimedia enabled devices. Through images, users generally present their daily activities, preferences or interests. This study aims to identify the way and extent to which personality differences, measured using the Big Five model, are related to online image posting and liking. In two experiments, the larger consisting of ~1.5 million Twitter images both posted and liked by ~4,000 users, we extract interpretable semantic concepts using large-scale image content analysis and analyze differences specific of each personality trait. Predictive results show that image content can predict personality traits, and that there can be significant performance gain by fusing the signal from both posted and liked images.

Real Men Don’t Say “Cute”: Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.

Studying the Dark Triad of Personality through Twitter Behavior

Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality -- the dark triad -- consisting of narcissism, psychopathy and Machiavellianism, is related to observable Twitter behavior such as platform usage, posted text and profile image choice. Our results show that we can map various behaviors to psychological theory and study new aspects related to social media usage. Finally, we build a machine learning algorithm that predicts the dark triad of personality in out-of-sample users with reliable accuracy.

Just between us: Exclusive communications in online social networks

ABSTRACT Social media websites such as Facebook are used for relationship development and maintenance often through self-disclosure and sharing of personal information. However, not all forms of social media communication may be equally suitable for this task. This paper explores users’ norms about the appropriateness of using private vs. public Facebook messages to communicate different kinds of personal information, and the effectiveness of these types of communication in building relationships. Study 1, a survey, revealed that users endorse conflicting expectations about preferences for receiving information publicly or privately. Study 2, a field experiment testing the effects of private versus public Facebook communications on actual relationship development using participants’ own Facebook pages, suggested that private messages lead to greater closeness.

Personality Driven Differences in Paraphrase Preference

Personality plays a decisive role in how people behave in different scenarios, including online social media. Researchers have used such data to study how personality can be predicted from language use. In this paper, we study phrase choice as a particular stylistic linguistic difference, as opposed to the mostly topical differences identified previously. Building on previous work on demographic preferences, we quantify differences in paraphrase choice from a massive Facebook data set with posts from over 115,000 users. We quantify the predictive power of phrase choice in user profiling and use phrase choice to study psycholinguistic hypotheses. This work is relevant to future applications that aim to personalize text generation to specific personality types.

Personality Profiles of Users Sharing Animal-related Content on Social Media

ABSTRACT Animal preferences are thought to be linked with more salient psychological traits of people, and most research examining owner personality as a differentiating factor has obtained mixed results. The rise in usage of social networks offers users a new medium in which they can broadcast their preferences and activities, including about animals. In two studies, the first on Facebook status updates and the second on images shared on Twitter, we revisited the link between Big Five personality traits and animal preference, specifically focusing on cats and dogs. We used automatic content analysis of text and images to unobtrusively measure preference for animals online using large datasets. In study 1, a dataset of Facebook status updates (n = 72,559) were analyzed and it was found that those who mentioned ownership of a cat (by using the phrase “my cat” (n = 5,053)) in their status updates were more open to experience, introverted, neurotic, and less conscientious when compared with the general population. Users mentioning ownership of a dog (by using “my dog” (n = 8,045)) were only less conscientious compared with the rest of the population. In study 2, a dataset of Twitter images was analyzed and revealed that users who featured either cat (n = 1,036) or dog (n = 1,499) images in their tweets were more neurotic, less conscientious, and less agreeable than those who did not. In addition, posting images containing cats was specific to users higher in openness, while posting images featuring dogs was associated with users higher in extraversion. These findings taken together align with some previous findings on the relationship between owner personality and animal preference, additionally highlighting some social media-specific behaviors.