Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach

An increasing number of museums and cultural institutions around the world use personalized, mostly mobile, museum guides to enhance visitor experiences. However since a typical museum visit may last a few minutes and visitors might only visit once, the personalization processes need to be quick and efficient, ensuring the engagement of the visitor. In this paper we investigate the use of indirect profiling methods through a visitor quiz, in order to provide the visitor with specific museum content. Building on our experience of a first study aimed at the design, implementation and user testing of a short quiz version at the Acropolis Museum, a second parallel study was devised. This paper introduces this research, which collected and analyzed data from two environments: the Acropolis Museum and social media (i.e. Facebook). Key profiling issues are identified, results are presented, and guidelines towards a generalized approach for the profiling needs of cultural institutions are discussed

Data-driven personalisation and the law - a primer: collective interests engaged by personalisation in markets, politics and law

Interdisciplinary Workshop on â��Data-Driven Personalisation in Markets, Politics and Law' on 28 June 2019Southampton Law School will be hosting an interdisciplinary workshop on the topic of â��Data-Driven Personalisation in Markets, Politics and Law' on Friday 28 June 2019, which will explore the pervasive and growing phenomenon of â��personalisationâ�� â�� from behavioural advertising in commerce and micro-targeting in politics, to personalised pricing and contracting and predictive policing and recruitment. This is a huge area which touches upon many legal disciplines as well as social science concerns and, of course, computer science and mathematics. Within law, it goes well beyond data protection law, raising questions for criminal law, consumer protection, competition and IP law, tort law, administrative law, human rights and anti-discrimination law, law and economics as well as legal and constitutional theory. Weâ��ve written a position paper, https://eprints.soton.ac.uk/428082/1/Data_Driven_Personalisation_and_the_Law_A_Primer.pdf which is designed to give focus and structure to a workshop that we expect will be strongly interdisciplinary, creative, thought-provoking and entertaining. We like to hear your thoughts! Call for papers! Should you be interested in disagreeing, elaborating, confirming, contradicting, dismissing or just reflecting on anything in the paper and present those ideas at the workshop, send us an abstract by Friday 5 April 2019 (Ms Clare Brady [email protected] ). We aim to publish an edited popular law/social science book with the most compelling contributions after the workshop.Prof Uta Kohl, Prof James Davey, Dr Jacob Eisler<br/

A System for Personality and Happiness Detection

This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness

A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots

We present a review of personality in neural conversational agents (CAs), also called chatbots. First, we define Personality, Persona, and Profile. We explain all personality schemes which have been used in CAs, and list models under the scheme(s) which they use. Second we describe 21 datasets which have been developed in recent CA personality research. Third, we define the methods used to embody personality in a CA, and review recent models using them. Fourth, we survey some relevant reviews on CAs, personality, and related topics. Finally, we draw conclusions and identify some research challenges for this important emerging field.Comment: 25 pages, 6 tables, 207 reference

Guide to Recruiting Black Men as Mentors for Black Boys

Black men are uniquely positioned to help guide black male youth to educational success and a productive future and through the barriers that stand in their way. But there are almost always more black boys to be mentored than black men to mentor them in formal mentoring programs. This guide helps mentoring programs engage in a productive and inclusive recruitment campaign by: 1) addressing program readiness; and 2) providing guidance on an effective social marketing campaign

What Encourages Purchase of Virtual Gifts in Live Streaming: Cognitive Absorption, Social Experience and Technological Environment

Live streaming has become extraordinarily popular worldwide. As a new form of social media, live streaming enables two levels of real-time interactions (i.e., between viewers and the streamer, and among viewers) and is monetized in a new way-viewers’ purchase of virtual gifts. The new monetization model has achieved a great success, yet there is a lack of understanding about what encourages viewers to purchase virtual gifts in live streaming. To explain such purchase behavior, this study develops a model which investigates the roles of viewers’ holistic experience with the system (i.e., cognitive absorption) and their social experiences (i.e., para-social interaction and virtual crowd experience), as well as how these experiences are developed within the technological environment of live streaming (i.e., interactivity, deep profiling and design aesthetics). The model was validated by using survey data collected from China. We also discuss implications for research and practice emerging out of this study

Applying data science and machine learning for psycho-demographic profiling of internet users

Dissertação de mestrado em Engenharia InformáticaThere always have been a huge interest in working with public data from online social media users, with the exponential growth of social media usage, this interest and re searches on the area keep increasing. This thesis aims to address prediction and classification tasks on online social net work data. The goal is to predict psycho-demographic - personality and demographic - traits by doing text emotion analysis on social networks as Twitter and Facebook. Our main motivation was to raise awareness to what can be done with users’ social media or network information or usual behaviours on the web, such as from text analysis we can trace their personality, know their tastes, how they behave and so on, and to spread the emotion-text relation on social networks subject, because it only started to be studied recently and there’s so much data and information to do it. To perform these tasks mentioned above we carried an extensive review of literature of previous works to define the state-of-art of the project and to learn and identify work strategies. Almost all of the past researches, based their results on a vast sample of users and data, but because some frameworks and APIs were shutdown in recent years, such as MyPersonality from Facebook adding to some frameworks being paid for, resulted in a small sample of users’ data to analyze in our thesis which can prejudice the results. We start by gathering data from Twitter and Facebook with users consent. On Twit ter we focused on tweets and retweets, on Facebook we focused on all of what the user typed by using the DataSelfie plugin that stored all that data on a server that can be retrieved later. Our next step was to find emotions on their text data with the help of a lexicon that categorized words by eight different emotions, two of them were put away because we focused only on the six major emotions - this is explained later - and we had to remove stopwords and apply stemming to all of the text and do a word-matching of every word of our data with every word from the lexicon. After this, we asked our participants to fulfill a "Big-Five" personality questionnaire and to provide us their age, so we added the Big-Five traits and age to each users individual dataset. We got their final versions, ready to apply machine-learning algorithms to find correlations between emotions and personality or demographic attributes. We focused on practical and methodological aspects of the user attribute prediction task. We used many techniques and algorithms that we thought it were best fit for the data we had and for the goal that we had to achieve. We gathered data in two datasets that we tested, one of them we called "Mixed Lan guage Dataset", contains all text entries from each user, and the other "User Dataset", contains one entry per user after we analyze every text entry for all users in order to have a more general view on each one. For the first mentioned dataset we achieve best results with the decision trees algorithms, from 58% on the agreeableness trait, to 68% on the neuroticism trait. This dataset had a problem with the way data was spread, so it was impossible to predict age and gender with efficiency. As for the lat ter, regarding demographic characteristics all of the classifiers had a good classifying percentage, from K-nearest’s 73% to Naive Bayes’ 95%. The most solid classifier for personality traits was the one using the CART decision tree algorithm, it ranged from 50% on the openness trait to 76% on the agreeableness one. There were classifiers with terrible results, there were others that were a bit dull, and there were some that stood out as we stated above. We had a small sample, and that was a problem as it wasn’t consistent or solid in terms of data value and that can change our results, we believe that our results would be way better if we applied the same mechanisms to a much bigger sample. Concluding, we demonstrate how we can predict personality or demographic traits - BigFive traits, age or gender - from studying emotions in text. As stated above, we hope this thesis will alert people for what can be done with their online information, we only focus on psycho-demographic profiling, but there are many other things that can be done.Sempre houve um enorme interesse em trabalhar com dados públicos dos utilizadores das redes sociais online, com o crescimento exponencial do uso das redes sociais, esse interesse e pesquisas na área continuam a crescer imenso. Esta tese tem como objetivo abordar tarefas de previsão e classificação de dados de redes sociais online. O objetivo é prever traços psico-demográficos - de personalidade e demográficos - fazendo análises de emoções presentes no texto em redes sociais como Twitter e Facebook. A nossa principal motivação foi consciencializar os utilizadores sobre o que pode ser feito com as informações dos utilizadores ou com os seus comportamentos na web, por exemplo, com a análise de texto, podemos traçar a sua personalidade, conhecer os seus gostos, saber como eles se comportam e assim por diante, e para espalhar a relação texto-emoções nas redes sociais, porque só começou a ser estudado recentemente e há imensos dados e informações para isso. Para realizar essas tarefas mencionadas acima, realizamos uma extensa revisão da literatura de trabalhos anteriores para definir o estado da arte do projeto, aprender e identificar estratégias de trabalho. Quase todas as pesquisas anteriores basearam os seus resultados numa vasta amostra de utilizadores e dados, mas como algumas frameworks e APIs foram encerradas nos últimos anos, como a MyPersonality do Facebook, adicionando a algumas frameworks que são pagas, o resultado foi que na nossa tese tivemos uma pequena amostra de dados de utilizadores para analisar o que pode prejudicar os resultados. Começamos por recolher os dados do Twitter e do Facebook com o consentimento dos utilizadores. No Twitter, concentramo-nos nos tweets e retweets, no Facebook concentramo-nos em tudo o que o utilizador digitou usando o plugin DataSelfie que armazena todos os dados num servidor que podem ser recuperados mais tarde. O nosso passo seguinte foi encontrar emoções no texto digitado por cada utilizador com a ajuda de um léxico que categoriza palavras por oito emoções diferentes, duas dessas emoções foram descartadas, concentrando-nos apenas nas seis principais emoções - o processo é explicado mais tarde - e tivemos que remover as stopwords e aplicar stemming a todo o texto e fazer uma correspondência de cada palavra dos nossos dados com cada palavra do léxico. Depois disto, pedimos aos nossos participantes que preenchessem um questionário de personalidade "Big-Five" e nos dessem a conhecer a sua idade. Adicionamos as 5 características do "Big-Five" e a idade ao dataset individual de cada utilizador e obtivemos as suas versões finais, prontas para aplicar algoritmos de aprendizagem de máquina para encontrar correlações entre as emoções e personalidade ou atributos demográficos. Focamo-nos nos aspectos práticos e metodológicos da tarefa de predição e classificação de atributos do utilizador. Muitas técnicas e algoritmos foram utilizados, aqueles que consideramos mais adequados para os dados que tínhamos e o objetivo que tínhamos que alcançar. Obtemos dados para dois datasets diferentes que testamos no final, um deles chamado de "Mixed Language Dataset", contém todas as entradas de texto de cada utilizador e o outro "User Dataset" contém uma entrada por utilizador após analisarmos todas as entradas de texto de todos eles para ter informação mais concisa geral sobre cada um. Para o primeiro conjunto de dados mencionado, os melhores resultados obtidos foram com os algoritmos de árvores de decisão, de 58% na característica de agreabilidade, para 68% na característica de neuroticismo. Este conjunto de dados tinha um problema com a forma como os dados estavam compostos no dataset, por isso foi impossível prever idade e género com eficiência. Quanto ao último dataset, em relação às características demográficas, todos os classificadores tiveram uma boa percentagem de classificação, de 73% de K-nearest para 95% com Naive Bayes. O classificador mais sólido para os traços de personalidade foi o que usou o algoritmo de árvore de decisão, CART, que varia apenas entre 50% no traço de "abertura a experiências" e 76% no de agreabilidade. Tivemos classificadores com resultados terríveis, houve outros que foram um pouco "aborrecidos", e houve alguns que se destacaram como afirmamos acima. A nossa amostra era consideravelmente pequena e isso foi um problema para nós, pois não era consistente ou sólido em termos de valores de dados e isso provavelmente alterou alguns dos nossos resultados, com uma amostra bem maior, mais profunda, acreditamos que aplicando os mesmos processos e mecanismos, teríamos resultados mais sólidos e mais consistentes. Concluindo, demonstramos como é possível prever traços de personalidade ou demográficos - traços BigFive, idade ou género - a partir do estudo de emoções presentes em texto. Como foi dito acima, esperamos que esta tese permita que os utilizadores tenham mais consciência da importância dos seus dados e do que conseguimos atingir com eles

Personality and susceptibility to political microtargeting: A comparison between a machine-learning and self-report approach

Based on recent technological advances, campaigners and political actors can use psychographic-based political marketing. Yet, empirical evidence about its effectiveness is still very limited. Based on self-congruity theory, a pre-registered experiment (N = 280) investigated the persuasion effects of personality-congruent political microtargeting on the attitude toward the political party and voting intentions of citizens. More precisely, the focus was on the thinking vs feeling personality dimension (MBTI), and it was tested whether this personality “interacts” with exposure to a matching advertising appeal: rational vs. emotional political ad. To do so, two different methodological approaches were used: 1) a machine learning approach; 2) a self-report survey measure of personality. Results revealed significant “congruence effects” between personality and ad appeal, and showed that perceived ad relevance was serving as the underlying mechanism (mediator). However, these results were only found when the self-report measure of personality was used. When the algorithmic approach was used, no significant results were found. These findings feed into timely societal, methodological, and theoretical contributions