Response Effects

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.

Personality is a combination of all the attributes—behavioral, temperamental, emotional, and mental—that characterizes a unique individual. Ability to identify personalities of people has always been of great interest to the researchers due to its importance. It continues to find highly useful applications in many domains. Owing to the increasing popularity of online social networks, researchers have started looking into the possibility of predicting a user's personality from his online social networking profile, which serves as a rich source of textual as well as non-textual content published by users. In the process of creating social networking profiles, users reveal a lot about themselves both in what they share and how they say it. Studies suggest that the online social networking websites are, in fact, a relevant and valid means of communicating personality. In this article, we review these various studies reported in literature toward identification of personality using o...

Personality profiling has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this survey, we show the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point out what has been gained and what lost during that trajectory, and show what can be realistic expectations in the field.

Previous studies highlighted spatial compatibility effects other than those strictly arising from stimulus-response locations. In particular, the so-called Destination Compatibility (DC) effect refers to faster responses for dynamic (i.e., moving) stimuli the end point of which is spatially compatible with the response key. Four experiments examined whether the DC effect also occurs with static visual stimuli symbolically representing either motion destination alone (Experiment 1a), or both motion origin and destination (Experiments 1b, 2a, and 2b). Overall, our results are consistent in showing a DC effect; most importantly, the present findings reveal a predominance of the effect of destination of motion over that of origin, even when both the starting and ending positions of the stimulus are symbolically represented and participants are instructed to respond according to motion origin. This finding suggests that the DC effect is independent from other stimulus-response compatibility (SRC) effects.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

English. Personality Computing from text has become popular in Natural Language Processing (NLP). For assessing gold-standard personality types, Big5 and MBTI are two popular models but still there is no comparison of the two in personality computing. With this paper, we provide for the first time a comparison of the two models from a computational perspective. To do that we exploit two multilingual datasets collected from Twitter in English, Italian, Spanish and Dutch. Italiano. Il riconoscimento automatico di personalità è diventato popolare nelle comunità di linguistica computazionale. I test Big Five e MBTI sono due modelli differenti per valutare la personalità, ma ancora non c’è un vero confronto dei due in ambito di riconoscimento automatico di personalità. In questo articolo per la prima volta forniamo una comparazione dei due modelli dal punto di vista computazionale. Per fare questo abbiamo raccolto dati Twitter in Inglese, Italiano, Spagnolo e Olandese in due corpora para...

With the development of social networks, a large variety of approaches have been developed to define users' personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%. INDEX TERMS Big 5, feature analysis, predicting personality, social behavior, social networks.

Previous studies highlighted spatial compatibility effects other than those strictly arising from stimulus-response locations. In particular, the so-called Destination Compatibility (DC) effect refers to faster responses for dynamic (i.e., moving) stimuli the end point of which is spatially compatible with the response key. Four experiments examined whether the DC effect also occurs with static visual stimuli symbolically representing either motion destination alone (Experiment 1a), or both motion origin and destination (Experiments 1b, 2a, and 2b). Overall, our results are consistent in showing a DC effect; most importantly, the present findings reveal a predominance of the effect of destination of motion over that of origin, even when both the starting and ending positions of the stimulus are symbolically represented and participants are instructed to respond according to motion origin. This finding suggests that the DC effect is independent from other stimulus-response compatibil...

Measuring public opinion on abortion is an ongoing concern for political scientists, mainly because the public does not always exhibit fixed attitudes on such topics. Most citizens express a centrist viewpoint between the pro choice and pro life extremes. These include a small group whose answers to abortion questions are so inconsistent that they give public officials an inaccurate measure of public opinion on this important issue. Inconsistent responses may result from context effects, such as the order in which the questions are asked or the way they are asked. Usually, researchers ask a battery of questions in which respondents say whether they approve of abortion generally and under a variety of circumstances, citing the reasons for which a woman might seek an abortion. This project includes an independent national survey using questions adopted from the General Social Survey. The sample is divided into four experimental groups with different question orders. Based on these findings, the recommended question order would be the one with the general question last and the remaining specific questions in a somewhat random pattern alternating between the so-called "hard" and "easy" individual abortion situations. One of the more surprising findings is that people didn't recognize themselves as subtracting the specific situations from the general question when it was asked first; hardly any said that was what they were doing when they gave inconsistent answers. Otherwise, about an equal number of respondents admitted answering the questions off the top of their heads as those who showed ambivalence by claiming they were deeply committed to their inconsistent responses. The study found most people who inconsistent on abortion are moderates leaning towards pro choice. Also, politically conservative regular church-goers can be just as inconsistent on abortion as the non-religious, non-political, low-educated non-church goers, especially if they are basically pro choice. Without a full understanding of who is generating inconsistent answers on abortion, some researchers may be tempted to eliminate these respondents from their sample. This research should allow them to understand these respondents better and develop better question wording and question orders to reduce their numbers.

The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user's personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the Big Five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.

Background / Introduction. This work explores the relationship between a person's demographic/psychological traits (e.g., gender, personality) and self-identity images and captions. Methods. We use a dataset of images and captions provided by N ≈ 1, 350 individuals, and we automatically extract features from both the images and captions. Results. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. Additionally, we consider the task of predicting gender and personality using both single-modality features and multimodal features. Conclusions. We show that a multimodal predictive approach outperforms purely visual methods and purely textual methods. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day (Meeker M, 2014. Internet trends 2014-Code conference. Retrieved May 28, 2014).

Character Computing consists of not only personality trait recognition, but also correlation among these traits. Tons of research has been conducted in this area. Various factors like demographics, sentiment, gender, LIWC, and others have been taken into account in order to understand human personality. In this paper, we have concentrated on the factors that could be obtained from available data using Natural Language Processing. It has been observed that the most successful personality trait prediction models are highly dependent on NLP techniques. Researchers across the globe have used different kinds of machine learning and deep learning techniques to automate this process. Different combinations of factors lead the research in different directions. We have presented a comparative study among those experiments and tried to derive a direction for future development.

Each individual has his own distinct character, making his own decisions which is based on his personality. Researchers in computer science field have tried to reach a model for extracting personality traits relying on user's profiles on social network sites as an input. Content created by users such as text posts, photos and shared activities in social network sites are considered as a huge source of data. Regarding user-created text, it has been proved that text pre-processing has a great impact if was applied to text before using it in research. In this paper, the effect of pre-processing (stemming and stop word removal) and adding numerical features is tested on the performance of Arabic personality prediction using AraPersonality dataset, which yielded 3.0% and 6.7% overall improvement to baseline experiments in binary representation and multiclass representation respectively.

Language provides an important source of information to predict human personality. However, most studies that have predicted personality traits using computational linguistic methods have focused on lexicon-based information. We investigate to what extent the performance of lexicon-based and grammarbased methods compare when predicting personality traits. We analyzed a corpus of student essays and their personality traits using two lexicon-based approaches, one top-down (Linguistic Inquiry and Word Count (LIWC)), one bottom-up (topic models) and one grammar-driven approach (Biber model), as well as combinations of these models. Results showed that the performance of the models and their combinations demonstrated similar performance, showing that lexicon-based topdown models and bottom-up models do not differ, and neither do lexicon-based models and grammar-based models. Moreover, combination of models did not improve performance. These findings suggest that predicting personality tr...

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. This paper reports an overview of the machine learning algorithms and the feature sets used, type of utilized footprint and the social media environments.

There is a huge interaction between users of various social media platforms. This communication produces enormous amount of user data worth to be analyzed from numerous aspects. One of the research area emerging from the user data is a major security issue known as cyberbullying. Since this problem has been recognized as the source of cybercrimes, design of a system to detect cyberbullying attacks/sources through the micro-blog texts is evident. Most of the academic search of this topic has been conducted in English language. The originality of this paper is that we develop an accurate cyberbullying detection system for Turkish language. We used data from Twitter to develop a supervised machine learning model on top of Bayesian Logistic Regression whose parameters are tuned with the use of grid-search algorithm. Since the text data produces a high dimensional training space for machine learning algorithms, we also used Chi-Squared (CH2) feature selection strategy to obtain best subset of features. The optimized version of the proposed algorithm on top of reduced feature dimension has produced an f-measure value of 0.925. Finally, we also compared the results of the proposed algorithm with the frequently used machine learning methods from literature and we provided the corresponding results in related sections.

Personality plays a fundamental role in human interaction. With the increasing amount of online user-generated content, automatic detection of a person's personality based on the text she produces is an important step to labeling and analyzing human behavior at a large scale. To date, most approaches to personality classification have modeled each personality trait in isolation (e.g., independent binary classification). In this paper, we instead model the dependencies between different personality traits using conditional random fields. Our study finds a correlation between Agreeableness and Emotional Stability traits that can improve Agreeableness classification. However, we also find that accuracy on other traits can degrade with this approach, due in part to the overall problem difficulty.

Personality plays a fundamental role in human interaction. With the increasing amount of online usergenerated content, automatic detection of a person's personality based on the text she produces is an important step to labeling and analyzing human behavior at a large scale. To date, most approaches to personality classification have modeled each personality trait in isolation (e.g., independent binary classification). In this paper, we instead model the dependencies between different personality traits using conditional random fields. Our study finds a correlation between Agreeableness and Emotional Stability traits that can improve Agreeableness classification. However, we also find that accuracy on other traits can degrade with this approach, due in part to the overall problem difficulty.

Today's world is witnessing great increase in the use of Social Media. People use them as a platform to share their feelings, emotions and experiences along with lot of personal information. All such information could be used in advantageous ways to help increase the Business and understand the user need. Personality prediction has gained lot of focus nowadays. It studies behavior of users and reflects the thinking, feelings etc. Traditional ways to take survey that was time consuming, for large number of users there is need of automatic prediction. Users are dynamic and can have their account on multiple platforms as they can have multi-context information. This survey gives an overview of different strategies used to predict the personality and behavior using the content available on social sites. Ability to predict user's personality traits can help to build many customized services or products. Finally, the last section gives the Future trends and directions.

The style in which a text is written re ects an array of meta-information concerning the text (e.g., topic, register, genre) and its author (e.g., gender, region, age, personality). The eld of stylometry addresses these aspects of style. A successful methodology, borrowed from text categorisation research, takes a two-stage approach which (i) achieves automatic selection of features with high predictive value for the categories to be learned, and (ii) uses machine learning algorithms to learn to categorize new documents by using the selected features (Sebastiani, 2002). To allow the selection of linguistic features rather than (n-grams of) terms, robust and accurate text analysis tools are necessary. Recently, language technology has progressed to a state of the art in which the systematic study of the variation of these linguistic properties in texts by different authors, time periods, regiolects, genres, registers, or even genders has become feasible.

With the development of social networks, a large variety of approaches have been developed to define users' personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%. INDEX TERMS Big 5, feature analysis, predicting personality, social behavior, social networks.

There are two conflicting perspectives regarding the relationship between profanity and dishonesty. These two forms of norm-violating behavior share common causes and are often considered to be positively related. On the other hand, however, profanity is often used to express one’s genuine feelings and could therefore be negatively related to dishonesty. In three studies, we explored the relationship between profanity and honesty. We examined profanity and honesty first with profanity behavior and lying on a scale in the lab (Study 1; N = 276), then with a linguistic analysis of real-life social interactions on Facebook (Study 2; N = 73,789), and finally with profanity and integrity indexes for the aggregate level of U.S. states (Study 3; N = 50 states). We found a consistent positive relationship between profanity and honesty; profanity was associated with less lying and deception at the individual level and with higher integrity at the society level.

The relationship between linguistic features of written texts and personality traits was investigated. Linguistic features used in this study were thematic (co-occurrence of the most frequent content words across participants), lexical (the maximum of new words) and syntactic (average sentence length). Personality traits were measured by VP+2 questionnaire standardized for Serbian population. Research was conducted on text materials collected from 114 Serbian participants (age 15-65), in their native tongue. Results showed that participants who gained low scores on Conscientiousness and high scores on Neuroticism and Negative Valence wrote about repeated daily activities and everyday life, but not about job-related matters or life perspective. Higher scores on Aggressiveness and Negative Valence coincided with writing about job-related matters and with the lower lexical richness. By showing that thematic content of text materials is affected by personality traits, these results support and expand previous findings regarding the relationship between personality and linguistic behaviour.

English. Are there correlations between language usage in conversations on Facebook and face to face meetings? To answer this question, we collected transcriptions from face to face multi-party conversations between 11 participants, and retrieved their Facebook threads. We automatically annotated the psycholinguistic dimensions in the two domains by means of the LIWC dictionary, and we performed correlation analysis. Results show that some Facebook dimensions, such as "likes" and shares, have a counterpart in face to face communication, in particular the number of questions and the length of statements. The corpus we collected has been anonymized and is be available for research purposes. Italiano. Ci sono correlazioni tra l'uso del linguaggio nelle conversazioni su Facebook e faccia a faccia? Per rispondere a questa domanda, abbiamo raccolto delle trascrizioni conversazioni di gruppo tra 11 partecipanti e campionato i loro dati Facebook. Abbiamo annotato automaticamente le dimensioni psicolinguistiche per mezzo del dizionario LIWC e abbiamo estratto le correlazioni tra le due diverse tipologie testuali. I risultati mostrano che alcune dimensioni linguistiche di Facebook, come i "mi piace" e il numero di condivisioni, correlano con dimensioni linguistiche dell'interazione faccia a faccia, come il numero di domande e la lunghezza delle frasi. Il corpus stato anonimizzato ed disponibile per scopi di ricerca.

Each individual has his own distinct character, making his own decisions which is based on his personality. Researchers in computer science field have tried to reach a model for extracting personality traits relying on user's profiles on social network sites as an input. Content created by users such as text posts, photos and shared activities in social network sites are considered as a huge source of data. Regarding user-created text, it has been proved that text pre-processing has a great impact if was applied to text before using it in research. In this paper, the effect of pre-processing (stemming and stop word removal) and adding numerical features is tested on the performance of Arabic personality prediction using AraPersonality dataset, which yielded 3.0% and 6.7% overall improvement to baseline experiments in binary representation and multiclass representation respectively.

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different online environments? and What is the decay in accuracy when porting models trained in one social media environment to another?

The Workshop on Computational Personality Recognition aims to define the state-of-the-art in the field and to provide tools for future standard evaluations in personality recognition tasks. In the WCPR14 we released two different datasets: one of Youtube Vlogs and one of Mobile Phone interactions. We structured the workshop in two tracks: an open shared task, where participants can do any kind of experiment, and a competition. We also distinguished two tasks: A) personality recognition from multimedia data, and B) personality recognition from text only. In this paper we discuss the results of the workshop.

Previous research investigating handle-response compatibility effects with graspable objects used different categories of objects as stimuli, regardless of their specific, intrinsic characteristics. The current study explores whether different types of objects' characteristics may elicit different types of spatial compatibility, that is, handle-response and response-effect compatibility as well as their potential interaction. In Experiment 1, objects having a graspable handle opposite to either a visible functional component (i.e., handle-function objects: a teapot) or a latent functional component (handle-only objects: a pitcher lacking the spout) were presented separately in different blocks. Both the handle and the goal-directed functional components of these objects were located on the horizontal axis. In Experiment 2, handle-only objects had a handle located on the horizontal axis and a latent functional component located on the vertical axis (e.g., a cup). In both experiments, participants were required to judge the material (plastic and metal) the object was made of. Results showed that the handle-response compatibility effect was sensitive to whether the actions consequences of object manipulation took place on the horizontal rather than on the vertical axis.

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available, 1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

https://www.ijert.org/personality-prediction-from-social-media-text-an-overview

https://www.ijert.org/research/personality-prediction-from-social-media-text-an-overview-IJERTV9IS050203.pdf

Today's world is witnessing great increase in the use of Social Media. People use them as a platform to share their feelings, emotions and experiences along with lot of personal information. All such information could be used in advantageous ways to help increase the Business and understand the user need. Personality prediction has gained lot of focus nowadays. It studies behavior of users and reflects the thinking, feelings etc. Traditional ways to take survey that was time consuming, for large number of users there is need of automatic prediction. Users are dynamic and can have their account on multiple platforms as they can have multi-context information. This survey gives an overview of different strategies used to predict the personality and behavior using the content available on social sites. Ability to predict user's personality traits can help to build many customized services or products. Finally, the last section gives the Future trends and directions.

The Workshop on Computational Personality Recognition aims to define the state-of-the-art in the field and to provide tools for future standard evaluations in personality recognition tasks. In the WCPR14 we released two different datasets: one of Youtube Vlogs and one of Mobile Phone interactions. We structured the workshop in two tracks: an open shared task, where participants can do any kind of experiment, and a competition. We also distinguished two tasks: A) personality recognition from multimedia data, and B) personality recognition from text only. In this paper we discuss the results of the workshop.

—Personality is a fundamental basis of human behavior. Personality affects the interaction and preferences of an individual. People are required to take a personality test to find out their personality. Social media is a place where users express themselves to the world. Posts made by users of social media can be analyzed to obtain their personal information. This experiment uses text classification to predict personality based on text written by Twitter users. The languages used are English and Indonesian. Classification methods implemented are Naive Bayes, K-Nearest Neighbors and Support Vector Machine. Testing results showed Naive Bayes slightly outperformed the other methods.

In this paper, we describe a multi-class text categorization approach to authorship attribution and test it on sets of e-mail collections. The PAN 2011 competition data consists of e-mails of variable length, written by various candidate authors, with some represented by significantly longer or more e-mails than others. Rather than construct a classifier for each separate author to discriminate it from the others (i.e. binary classification), we adopt a multi-class scheme where all authorship classes are learned simultaneously. We explore the effect of the selection of feature types and of the C parameter in the SVM multiclass learning algorithm. Variable-length lexical features showed promising results, nevertheless our authorship attribution approach only scored a mid position amongst the other competitors, for the SMALL as well as the LARGE test sets.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users' personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users' status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users' personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users' status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

As participant recruitment and data collection over the Internet have become more common, numerous observers have expressed concern regarding the validity of research conducted in this fashion. One growing method of conducting research over the Internet involves recruiting participants and administering questionnaires over Facebook, the world's largest social networking service. If Facebook is to be considered a viable platform for social research, it is necessary to demonstrate that Facebook users are sufficiently heterogeneous and that research conducted through Facebook is likely to produce results that can be generalized to a larger population. The present study examines these questions by comparing demographic and personality data collected over Facebook with data collected through a standalone website, and data collected from college undergraduates at two universities. Results indicate that statistically significant differences exist between Facebook data and the comparison data-sets, but since 80% of analyses exhibited partial η 2 < .05, such differences are small or practically nonsignificant in magnitude. We conclude that Facebook is a viable research platform, and that recruiting Facebook users for research purposes is a promising avenue that offers numerous advantages over traditional samples.

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. This paper reports an overview of the machine learning algorithms and the feature sets used, type of utilized footprint and the social media environments.

We report a perception study in which judges attempted to label as deceptive or truthful the recorded interviews of the Columbia-SRI-Colorado Corpus of deceptive speech. In general, judges performed very poorly at the task, scoring on average worse than chance. We use these results to contextualize 'current best' machine learning performance on this corpus. In addition, we report strong findings that suggest that certain personality factors influence the ability of a judge to detect deception in speech.

Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using common sense knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined common sense knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of common sense knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.

The appearance of the so-called recommender systems has led to the possibility of reducing the information overload experienced by individuals searching among online resources. One of the areas of application of recommender systems is the online tourism domain where sites like TripAdvisor allow people to post reviews of various hotels to help others make a good choice when planning their trip. As the number of such reviews grows in size every day, clearly it is impractical for the individual to go through all of them. We propose the TWIN ("Tell me What I Need") Personality-based Recommender System that analyzes the textual content of the reviews and estimates the personality of the user according to the Big Five model to suggest the reviews written by "twin-minded" people. In this paper we compare a number of algorithms to select the better option for personality estimation in the task of user profile construction.

The Workshop on Computational Personality Recognition aims to define the state-of-the-art in the field and to provide tools for future standard evaluations in personality recognition tasks. In the WCPR14 we released two different datasets: one of Youtube Vlogs and one of Mobile Phone interactions. We structured the workshop in two tracks: an open shared task, where participants can do any kind of experiment, and a competition. We also distinguished two tasks: A) personality recognition from multimedia data, and B) personality recognition from text only. In this paper we discuss the results of the workshop.

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.

Abstract In this paper, we target the automatic recognition of personality states in a meeting scenario employing visual and acoustic features. The social psychology literature has coined the name personality state to refer to a specific behavioral episode wherein a person behaves as more or less introvert/extrovert, neurotic or open to experience, etc. Personality traits can then be reconstructed as density distributions over personality states.