Elaborando um modelo de marca de lugar gênero-neutro: o caso de 'Doll Village' na Índia

Gender perspective has been widely discussed in scholarly literature in connection with place branding. While assigning personality to ‘places as branded entities’ the socially constructed gender norms were often used. Empirical studies have showcased the gender power ascribed to places in stimulating decision-making and pulling visitors. However, society, as it is in constant flux, is drifting away from ubiquitous norms governing gender and sexuality and challenging the conventional gender identities and sexual orientation. These perceived value-shift has reflected in the marketing aspects as brands were stripped off its gender identity. Modern brands do not embody the aspirational notions of consumers, but are also reflective of social movements. This study focuses on developing a gender-neutral place brand model with intangible cultural heritage (ICH) as the core place asset. Prior studies have shown that traditional crafts and craftsmanship as ICH could be used to promote a place. However, ICH has not been considered as a place branding element that could neutralize gender perception for a place. The study was conducted at Natungram, a village in the district of West Bengal, India, known for its legacy of crafting wooden dolls. The study used a mixed method research (MMR) approach and used crossover analysis framework to assess the data. Five gender-neutral brand dimensions were identified, namely relational, participative, sensory, behavioural and cognitive with a 16 scale-item instrument being validated through confirmatory factor analysis. The spatial and phenomenal segregation index also confirmed a predominant gender-neutral perception amongst the visitors. Much of this could be attributed to the participation of the stakeholders of ICH of Natungram irrespective of their gender identity. This gender blurring effect also reflected in visitors’ engagement in co-creation activities which conformed with the theory of immersive experience. The study implicates In future this study could be expanded to incorporate other place assets which could be moulded into non-binary brand elements and should be conducted in other places with unique assets to check the transferability of the present study.La perspectiva de género se ha debatido ampliamente en la literatura académica en relación con la marca de lugar. Al asignar personalidad a los “lugares como entidades de marca”, se han utilizado a menudo las normas de género construidas socialmente. Los estudios empíricos han puesto de manifiesto el poder de género atribuido a los lugares a la hora de estimular la toma de decisiones y atraer visitantes. Sin embargo, la sociedad, en constante cambio, se está alejando de las omnipresentes normas que rigen el género y la sexualidad y desafía las identidades de género y la orientación sexual convencionales. Este cambio de valores percibido se ha reflejado en los aspectos de marketing, ya que las marcas se han despojado de su identidad de género. Las marcas modernas no encarnan las nociones aspiracionales de los consumidores, sino que también reflejan los movimientos sociales. Este estudio se centra en el desarrollo de un modelo de marca de lugar neutro desde el punto de vista del género con el patrimonio cultural inmaterial (PCI) como principal activo del lugar. Estudios anteriores han demostrado que la artesanía tradicional puede utilizarse para promocionar un lugar. Sin embargo, el PCI no se ha considerado como un elemento de marca de lugar que pueda neutralizar la percepción de género de un lugar. El estudio se llevó a cabo en Natungram, un pueblo del distrito de Bengala Occidental, India, conocido por su legado de artesanía de muñecas de madera. El estudio utilizó un enfoque de investigación de método mixto (MMR) y empleó un marco de análisis cruzado para evaluar los datos. Se identificaron cinco dimensiones de marca neutrales desde el punto de vista del género, a saber, relacional, participativa, sensorial, conductual y cognitiva, con un instrumento de 16 ítems de escala que se validó mediante un análisis factorial confirmatorio. El índice de segregación espacial y fenoménica también confirmó una percepción predominante de neutralidad de género entre los visitantes. Gran parte de ello podría atribuirse a la participación de las partes interesadas en el PCI de Natungram, independientemente de su identidad de género. Este efecto de desdibujamiento de género también se reflejó en la participación de los visitantes en actividades de cocreación que se ajustaban a la teoría de la experiencia inmersiva. En el futuro, este estudio podría ampliarse para incorporar otros activos del lugar que pudieran moldearse como elementos de marca no binarios, y debería llevarse a cabo en otros lugares con activos únicos para comprobar la transferibilidad del presente estudio.A perspectiva de gênero tem sido amplamente discutida na literatura acadêmica em conexão com a marcação de lugares. Enquanto se atribui personalidade a 'lugares como entidades marcadas', as normas de gênero socialmente construídas são frequentemente utilizadas. Estudos empíricos têm mostrado o poder de gênero atribuído aos lugares em estimular a tomada de decisões e atrair visitantes. No entanto, a sociedade, em constante mudança, está se afastando das normas ubíquas que regem o gênero e a sexualidade, desafiando as identidades de gênero convencionais e a orientação sexual. Essa mudança percebida de valores tem refletido nos aspectos de marketing, já que as marcas foram despojadas de sua identidade de gênero. As marcas modernas não apenas incorporam as noções aspiracionais dos consumidores, mas também são reflexo dos movimentos sociais. Este estudo concentra-se no desenvolvimento de um modelo de marca de lugar gênero-neutro com patrimônio cultural intangível (PCI) como o ativo central do lugar. Estudos anteriores mostraram que artesanatos tradicionais e habilidades manuais como PCI poderiam ser usados para promover um lugar. No entanto, o PCI não foi considerado como um elemento de marca de lugar que poderia neutralizar a percepção de gênero para um local. O estudo foi conduzido em Natungram, uma vila no distrito de West Bengal, Índia, conhecida por sua herança na confecção de bonecas de madeira. O estudo usou uma abordagem de pesquisa de método misto (RMM) e usou uma estrutura de análise cruzada para avaliar os dados. Foram identificadas cinco dimensões de marca gênero-neutro, nomeadamente relacional, participativa, sensorial, comportamental e cognitiva, com um instrumento de 16 itens de escala validado por meio de análise fatorial confirmatória. O índice de segregação espacial e fenomenal também confirmou uma percepção predominante de gênero-neutro entre os visitantes. Grande parte disso pode ser atribuída à participação dos stakeholders do PCI de Natungram, independentemente de sua identidade de gênero. Esse efeito de embaçamento de gênero também se refletiu no envolvimento dos visitantes em atividades de co-criação, o que se alinha com a teoria da experiência imersiva. O estudo implica que, no futuro, este estudo pode ser expandido para incorporar outros ativos do local que poderiam ser moldados em elementos de marca não-binários e deve ser conduzido em outros lugares com ativos únicos para verificar a transferibilidade do presente estudo

Identifying the topic-specific influential users in Twitter

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. In more detail, from a collection of tweets matching a specified query, we want to detect the influential users, in an online fashion. In order to address this objective, we first want to focus our search on the individuals who write in their personal accounts, so we investigate how we can differentiate between the personal and non-personal accounts. Secondly, we investigate which set of features can best lead us to the topic-specific influential users, and how these features can be expressed in a model to produce a ranked list of influential users. Finally, we look into the use of the language and if it can be used as a supporting feature for detecting the author\u27s influence. In order to decide on how to differentiate between the personal and non-personal accounts, we compared between the effectiveness of using SVM and using a manually assembled list of the non-personal accounts. In order to decide on the features that can best lead us to the influential users, we ran a few experiments on a set of features inspired from the literature. Two ranking methods were then developed, using feature combinations, to identify the candidate users for being influential. For evaluation we manually examined the users, looking at their tweets and profile page in order to decide on their influence. To address our final objective, we ran a few experiments to investigate if the SLM could be used to identify the influential users\u27 tweets. For user account classification into personal and non-personal accounts, the SVM was found to be domain independent, reliable and consistent with a precision of over 0.9. The results showed that over time the list performance deteriorates and when the domain of the test data was changed, the SVM performed better than the list with higher precision and specificity values. We extracted eight independent features from a set of 12, and ran experiments on these eight and found that the best features at identifying influential users to be the Followers count, the Average Retweets count, The Average Retweets Frequency and the Age_Activity combination. Two ranking methods were developed and tested on a set of tweets retrieved using a specific query. In the first method, these best four features were combined in different ways. The best combination was the one that took the average of the Followers count and the Average Retweets count, producing a precision at 10 value of 0.9. In the second method, the users were ranked according to the eight independent features and the top 50 users of each were included in separate lists. The users were then ranked according to their appearance frequency in these lists. The best result was obtained when we considered the users who appeared in six or more of the lists, which resulted in a precision of 1.0. Both ranking methods were then conducted on 20 different collections of retrieved tweets to verify their effectiveness in detecting influential users, and to compare their performance. The best result was obtained by the second method, for the set of users who appeared in six or more of the lists, with the highest precision mean of 0.692. Finally, for the SLM, we found a correlation between the users\u27 average Retweets counts and their tweets\u27 perplexity values, which consolidates the hypothesis that SLM can be trained to detect the highly retweeted tweets. However, the use of the perplexity for identifying influential users resulted in very low precision values. The contributions of this thesis can be summarized into the following. A method to classify the personal accounts was proposed. The features that help detecting influential users were identified to be the Followers count, the Average Retweets count, the Average Retweet Frequency and the Age_Activity combination. Two methods for identifying the influential users were proposed. Finally, the simplistic approach using SLM did not produce good results, and there is still a lot of work to be done for the SLM to be used for identifying influential users

Multi-Industry Simplex : A Probabilistic Extension of GICS

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.Comment: 17 pages, 10 figure

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years

The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation

Abstract We document marked trends in 10-K disclosure over the period 1996–2013, with increases in length, boilerplate, stickiness, and redundancy and decreases in specificity, readability, and the relative amount of hard information. We use Latent Dirichlet Allocation (LDA) to examine specific topics and find that new FASB and SEC requirements explain most of the increase in length and that 3 of the 150 topics—fair value, internal controls, and risk factor disclosures—account for virtually all of the increase. These three disclosures also play a major role in explaining the trends in the remaining textual characteristics

Community and Thread Methods for Identifying Best Answers in Online Question Answering Communities

Much research has recently investigated the measurement of quality answers in Question Answering (Q&A) communities in the form of automatic best answer identification. Previous approaches have focused on manual user annotations and diverse features based on intuition for identifying best answers and proved relatively successful despite considering best answer identification as a general classification problem. Best answer modelling is generally distanced from community studies about what users regard as important for identifying quality content. In particular, previous research tends to only focus on the automatic aspects of best answers identification model by applying generic learning algorithms. This thesis introduces the concepts of qualitative and structural design in order to investigate if features derived from community questionnaires can enrich the understanding of best answer identification in Q&A communities and if the thread-like structure of Q&A communities can be exploited for better results. Two different approaches for exploiting the thread structure of Q&A communities are proposed and two new, previously unstudied, features are introduced. First, a measure of question complexity is introduced as a proxy measure of answerer knowledge. Second, different models of contribution effort are proposed for representing the answering reactivity of contributors. The experiments are systematically conducted on datasets issued from three different communities that vary in size, content and structure. The results show that the newly proposed features allow for better understanding of what constitute best answers. The findings also reveal that the thread-wise algorithms and optimisation techniques created from the structural design methodology correlate with best answers. In general both structural and qualitative design appear to improve best answer identification meaning that structural and qualitative methods may improve unrelated classification tasks

Composing Deep Learning and Bayesian Nonparametric Methods

Recent progress in Bayesian methods largely focus on non-conjugate models featured with extensive use of black-box functions: continuous functions implemented with neural networks. Using deep neural networks, Bayesian models can reasonably fit big data while at the same time capturing model uncertainty. This thesis targets at a more challenging problem: how do we model general random objects, including discrete ones, using random functions? Our conclusion is: many (discrete) random objects are in nature a composition of Poisson processes and random functions}. Thus, all discreteness is handled through the Poisson process while random functions captures the rest complexities of the object. Thus the title: composing deep learning and Bayesian nonparametric methods. This conclusion is not a conjecture. In spacial cases such as latent feature models , we can prove this claim by working on infinite dimensional spaces, and that is how Bayesian nonparametric kicks in. Moreover, we will assume some regularity assumptions on random objects such as exchangeability. Then the representations will show up magically using representation theorems. We will see this two times throughout this thesis. One may ask: when a random object is too simple, such as a non-negative random vector in the case of latent feature models, how can we exploit exchangeability? The answer is to aggregate infinite random objects and map them altogether onto an infinite dimensional space. And then assume exchangeability on the infinite dimensional space. We demonstrate two examples of latent feature models by (1) concatenating them as an infinite sequence (Section 2,3) and (2) stacking them as a 2d array (Section 4). Besides, we will see that Bayesian nonparametric methods are useful to model discrete patterns in time series data. We will showcase two examples: (1) using variance Gamma processes to model change points (Section 5), and (2) using Chinese restaurant processes to model speech with switching speakers (Section 6). We also aware that the inference problem can be non-trivial in popular Bayesian nonparametric models. In Section 7, we find a novel solution of online inference for the popular HDP-HMM model