The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

Temporal dynamics in information retrieval

The passage of time is unrelenting. Time is an omnipresent feature of our existence, serving as a context to frame change driven by events and phenomena in our personal lives and social constructs. Accordingly, various elements of time are woven throughout information itself, and information behaviours such as creation, seeking and utilisation. Time plays a central role in many aspects of information retrieval (IR). It can not only distinguish the interpretation of information, but also profoundly influence the intentions and expectations of users' information seeking activity. Many time-based patterns and trends - namely temporal dynamics - are evident in streams of information behaviour by individuals and crowds. A temporal dynamic refers to a periodic regularity, or, a one-off or irregular past, present or future of a particular element (e.g., word, topic or query popularity) - driven by predictable and unpredictable time-based events and phenomena. Several challenges and opportunities related to temporal dynamics are apparent throughout IR. This thesis explores temporal dynamics from the perspective of query popularity and meaning, and word use and relationships over time. More specifically, the thesis posits that temporal dynamics provide tacit meaning and structure of information and information seeking. As such, temporal dynamics are a ‘two-way street’ since they must be supported, but also conversely, can be exploited to improve time-aware IR effectiveness. Real-time temporal dynamics in information seeking must be supported for consistent user satisfaction over time. Uncertainty about what the user expects is a perennial problem for IR systems, further confounded by changes over time. To alleviate this issue, IR systems can: (i) assist the user to submit an effective query (e.g., error-free and descriptive), and (ii) better anticipate what the user is most likely to want in relevance ranking. I first explore methods to help users formulate queries through time-aware query auto-completion, which can suggest both recent and always popular queries. I propose and evaluate novel approaches for time-sensitive query auto-completion, and demonstrate state-of-the-art performance of up to 9.2% improvement above the hard baseline. Notably, I find results are reflected across diverse search scenarios in different languages, confirming the pervasive and language agnostic nature of temporal dynamics. Furthermore, I explore the impact of temporal dynamics on the motives behind users' information seeking, and thus how relevance itself is subject to temporal dynamics. I find that temporal dynamics have a dramatic impact on what users expect over time for a considerable proportion of queries. In particular, I find the most likely meaning of ambiguous queries is affected over short and long-term periods (e.g., hours to months) by several periodic and one-off event temporal dynamics. Additionally, I find that for event-driven multi-faceted queries, relevance can often be inferred by modelling the temporal dynamics of changes in related information. In addition to real-time temporal dynamics, previously observed temporal dynamics offer a complementary opportunity as a tacit dimension which can be exploited to inform more effective IR systems. IR approaches are typically based on methods which characterise the nature of information through the statistical distributions of words and phrases. In this thesis I look to model and exploit the temporal dimension of the collection, characterised by temporal dynamics, in these established IR approaches. I explore how the temporal dynamic similarity of word and phrase use in a collection can be exploited to infer temporal semantic relationships between the terms. I propose an approach to uncover a query topic's "chronotype" terms -- that is, its most distinctive and temporally interdependent terms, based on a mix of temporal and non-temporal evidence. I find exploiting chronotype terms in temporal query expansion leads to significantly improved retrieval performance in several time-based collections. Temporal dynamics provide both a challenge and an opportunity for IR systems. Overall, the findings presented in this thesis demonstrate that temporal dynamics can be used to derive tacit structure and meaning of information and information behaviour, which is then valuable for improving IR. Hence, time-aware IR systems which take temporal dynamics into account can better satisfy users consistently by anticipating changing user expectations, and maximising retrieval effectiveness over time

Social impact retrieval: measuring author influence on information retrieval

The increased presence of technologies collectively referred to as Web 2.0 mean the entire process of new media production and dissemination has moved away from an authorcentric approach. Casual web users and browsers are increasingly able to play a more active role in the information creation process. This means that the traditional ways in which information sources may be validated and scored must adapt accordingly. In this thesis we propose a new way in which to look at a user's contributions to the network in which they are present, using these interactions to provide a measure of authority and centrality to the user. This measure is then used to attribute an query-independent interest score to each of the contributions the author makes, enabling us to provide other users with relevant information which has been of greatest interest to a community of like-minded users. This is done through the development of two algorithms; AuthorRank and MessageRank. We present two real-world user experiments which focussed around multimedia annotation and browsing systems that we built; these systems were novel in themselves, bringing together video and text browsing, as well as free-text annotation. Using these systems as examples of real-world applications for our approaches, we then look at a larger-scale experiment based on the author and citation networks of a ten year period of the ACM SIGIR conference on information retrieval between 1997-2007. We use the citation context of SIGIR publications as a proxy for annotations, constructing large social networks between authors. Against these networks we show the effectiveness of incorporating user generated content, or annotations, to improve information retrieval

(In)authentic Participation: Contemporary Participatory Performance Practice, Social Media & Neoliberalism

There has been a (re)emergence of participatory modes of audience engagement in contemporary western European theatre and performance since the mid-2000s. The concern at the root of contemporary participatory performance practice is the authenticity or inauthenticity with which one constitutes and presents one’s self in contemporary digitised society. Social media technology is not inauthentic in of itself, however the wider political economy in which social media operates encourages users to participate in a way that promotes the constitution, maintenance and presentation of a consistent, static and commodifiable self. Participation in social media in the context of neoliberalism makes users vulnerable to external influence and manipulates them into disengaging with their fundamental agency while promoting an ideology of choice and self-creation. Contemporary participatory performance practice problematises this inauthentic orientation by appropriating, reflecting and critically amplifying both social media technologies and modes of participation inherent in neoliberally induced social media. Contemporary practice also provides participatory alternatives that help audiences approach selfhood from an authentic orientation, embracing individual agency, responsibility and a liminal position between internal intention and external influence. This thesis draws upon the phenomenological ontology of Martin Heidegger and Jean-Paul Sartre to investigate the question of why this has come to pass by exploring the recent changes to the way that one presents oneself and interacts with other people through online social media

Click: Exploring Social Actions in the 21st Century

The phenomenon of social media has become part of our everyday interaction with others. At the heart of this exploration lies the idea that ‘clicks’ on social media are meaningful ways of communicating with others and doing things together. Approaching them in their role as a symbol of 21st century everyday social action means considering how ‘clicks’ are understood as meaningful units of behaviour in context. Whereas the standard philosophy of action tempts one to think of a ‘click’ as a highly individualistic action, I contend that even the simplest ‘click’ retains important senses of sociality. Philosophical theories struggle to explain these nuances of social action and ignore how sociality weaves through conceptions of actions, offline and online. I criticise views in philosophy that are enchanted by a solipsistic paradigm of action. I call this point of view atomism. The atomistic paradigm has been uncritically employed in accounts of collective action, which proposes a particular way of making the distinction between actions performed individually, and action that is performed socially or collectively. The former is traditionally taken to be preliminary to the latter. This has led to a misguided understanding of the connection between sociality and action, resulting in unnecessary metaphysical tangles. As an alternative, I propose a view that illuminates how actions are social regardless as to whether they are performed with others or alone. An action’s sociality can but is not necessarily constituted by the number of agents involved at any given time. Sociality rather, interweaves conceptions of action on a conceptual level. To highlight the nuances of the interplay of sociality and action, I suggest social holism as a perspicuous perspective from which actions are conceptualised in the light of an inquisitive aim. It explores how actions are ordinarily understood in relation to what they mean. What the action is thus, depends on the context and is informed by senses of sociality. This ordinary action philosophy investigates the phenomenological engagement of ordinary agents with actions and outlines the social resources that are active in understanding each other’s actions. Where atomism might tempt one to think of a ‘click’ on social media as an individual action in isolation, I show that understanding ‘clicks’ as actions is already social, both on empirical and conceptual levels. Reaching from being accustomed to a world in which this form of communication is conceivably doable to the ways in which the opportunity to do so is a socially enabled achievement. The argument will proceed in three steps: first, drafting a holistic perspective on how agents come to see action as meaningful wholes. Second, showing how perceptual holism is informed by senses of sociality: paradigmatically, one encounters actions in a variety of social ways. These include the understanding of doable actions that are ordinary and repeatable; the sense in which actions are interactive and enabled by others. With these senses in place, and by drawing on a range of methodological allies outside the philosophy of action, an exploration of the acts of communication performed on social media is presented as demonstrating how actions are understood, described and rendered intelligible. And third, applying the senses of sociality to everyday examples of social media action. Starting from social holism avoids caveats of atomism; paradigms of social action collapses the artificial distinction that atomists make between individual and collective action. Embracing this alternative starting point lays the groundwork for conducting further research in the area, particularly internet ethics and considerations of online action

Report on the 2015 NSF Workshop on Unified Annotation Tooling

On March 30 & 31, 2015, an international group of twenty-three researchers with expertise in linguistic annotation convened in Sunny Isles Beach, Florida to discuss problems with and potential solutions for the state of linguistic annotation tooling. The participants comprised 14 researchers from the U.S. and 9 from outside the U.S., with 7 countries and 4 continents represented, and hailed from fields and specialties including computational linguistics, artificial intelligence, speech processing, multi-modal data processing, clinical & medical natural language processing, linguistics, documentary linguistics, sign-language linguistics, corpus linguistics, and the digital humanities. The motivating problem of the workshop was the balkanization of annotation tooling, namely, that even though linguistic annotation requires sophisticated tool support to efficiently generate high-quality data, the landscape of tools for the field is fractured, incompatible, inconsistent, and lacks key capabilities. The overall goal of the workshop was to chart the way forward, centering on five key questions: (1) What are the problems with current tool landscape? (2) What are the possible benefits of solving some or all of these problems? (3) What capabilities are most needed? (4) How should we go about implementing these capabilities? And, (5) How should we ensure longevity and sustainability of the solution? I surveyed the participants before their arrival, which provided significant raw material for ideas, and the workshop discussion itself resulted in identification of ten specific classes of problems, five sets of most-needed capabilities. Importantly, we identified annotation project managers in computational linguistics as the key recipients and users of any solution, thereby succinctly addressing questions about the scope and audience of potential solutions. We discussed management and sustainability of potential solutions at length. The participants agreed on sixteen recommendations for future work. This technical report contains a detailed discussion of all these topics, a point-by-point review of the discussion in the workshop as it unfolded, detailed information on the participants and their expertise, and the summarized data from the surveys

Opinion Detection, Sentiment Analysis and User Attribute Detection from Online Text Data

With the growing increase in the use of the internet in most parts of the world today, users generate significant amounts of online text on different platforms such as online social networks, product review websites, travel blogs, to name just a few. The variety of content on these platforms has made them an important resource for researchers to gauge user activity, determine their opinions and analyze their behavior, without having to perform monetarily and temporally expensive surveys. Gaining insights into user behavior enables us to better understand their likes and dislikes, which in turn is helpful for economic purposes such as marketing, advertising and recommendations. Further, owing to the fact that online social networks have recently been instrumental in socio-political revolutions such as the Arab Spring, and for awareness-generation campaigns by MoveOn.org and Avaaz.org, analysis of online data can uncover user preferences. The overarching goal of this Ph.D. thesis is to pose some research questions and propose solutions, mostly pertaining to user opinions and attributes, keeping in mind the large quantities of noise present in online textual data. This thesis illustrates that with the extraction of informative textual features and the use of robust NLP and machine learning techniques, it is possible to perform efficient signal extraction from online text data, and use it to better understand user behavior. The first research problem addressed is that of opinion detection and sentiment analysis of users on a given topic, from their self-generated tweets. The key idea is to select relevant hashtags and n-grams using an $l_1$-regularized logistic regression model for opinion detection. The second research problem deals with temporal opinion detection from tweets, i.e., detecting user opinions on a topic in which the conversation evolves over time. For instance, on the widely-discussed topic of Obamacare (the Affordable Care Act in the U.S.), various issues became the focal points of discussion among users over time, as corresponding socio-political events and occurrences took place in real-time. We propose a machine-learning model based on seminal work from the sociological literature that is based on the premise that most opinion changes occur slowly over time. Our model is able to successfully capture opinions over time using publicly available tweets, as well as to uncover the key points of discussion as time progresses. In the third research problem, we utilize distributed representation of words in a method that determines, from user reviews, aspects of products and services that users like and dislike. We harness the contextual similarity between words and effectively build meta-features that capture user sentiment at a granular level. Finally in the fourth research problem, we propose a method to detect the age of users from their publicly available tweets. Using a method based on distributed representation of words and clustering, we are able to achieve high accuracies in age detection, as well as to simultaneously discover topics of conversation in which users of different age groups engage

Expanding Data Imaginaries in Urban Planning:Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

“Expanding Data Imaginaries in Urban Planning” synthesizes more than three years of industrial research conducted within Gehl and the Techno–Anthropology Lab at Aalborg University. Through practical experiments with social media images, digital photovoice, and participatory mapmaking, the project explores how visual materials created by citizens can be used within a digital and participatory methodology to reconfigure the empirical ground of data-driven urbanism. Drawing on a data feminist framework, the project uses visual research to elevate community voices and situate urban issues in lived experiences. As a Science and Technology Studies project, the PhD also utilizes its industrial position as an opportunity to study Gehl’s practices up close, unpacking collectively held narratives and visions that form a particular “data imaginary” and contribute to the production and perpetuation of the role of data in urban planning. The dissertation identifies seven epistemological commitments that shape the data imaginary at Gehl and act as discursive closures within their practice. To illustrate how planners might expand on these, the dissertation uses its own data experiments as speculative demonstrations of how to make alternative modes of knowing cities possible through participatory and digital visual methods