996 resultados para Social Analytics


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Twitter is the focus of much research attention, both in traditional academic circles and in commercial market and media research, as analytics give increasing insight into the performance of the platform in areas as diverse as political communication, crisis management, television audiencing and other industries. While methods for tracking Twitter keywords and hashtags have developed apace and are well documented, the make-up of the Twitter user base and its evolution over time have been less understood to date. Recent research efforts have taken advantage of functionality provided by Twitter's Application Programming Interface to develop methodologies to extract information that allows us to understand the growth of Twitter, its geographic spread and the processes by which particular Twitter users have attracted followers. From politicians to sporting teams, and from YouTube personalities to reality television stars, this technique enables us to gain an understanding of what prompts users to follow others on Twitter. This article outlines how we came upon this approach, describes the method we adopted to produce accession graphs and discusses their use in Twitter research. It also addresses the wider ethical implications of social network analytics, particularly in the context of a detailed study of the Twitter user base.

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It might still sound strange to dedicate an entire journal issue exclusively to a single internet platform. But it is not the company Twitter Inc. that draws our attention; this issue is not about a platform and its features and services. It is about its users and the ways in which they interact with one another via the platform, about the situations that motivate people to share their thoughts publicly, using Twitter as a means to reach out to one another. And it is about the digital traces people leave behind when interacting with Twitter, and most of all about the ways in which these traces – as a new type of research data – can also enable new types of research questions and insights.

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We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. Core to the toolkit is the notion of learner access to their own data. A number of implementational issues are discussed, and an ontology of xAPI verb/object/activity statements as they might be unified across 7 different social media and online environments is introduced. After considering some of the analytics that learners might be interested in discovering about their own processes (the delivery of which is prioritised for the toolkit) we propose a set of learning activities that could be easily implemented, and their data tracked by anyone using the toolkit and a LRS.

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An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

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This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.

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An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

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This paper discusses how global financial institutions are using big data analytics within their compliance operations. A lot of previous research has focused on the strategic implications of big data, but not much research has considered how such tools are entwined with regulatory breaches and investigations in financial services. Our work covers two in-depth qualitative case studies, each addressing a distinct type of analytics. The first case focuses on analytics which manage everyday compliance breaches and so are expected by managers. The second case focuses on analytics which facilitate investigation and litigation where serious unexpected breaches may have occurred. In doing so, the study focuses on the micro/data to understand how these tools are influencing operational risks and practices. The paper draws from two bodies of literature, the social studies of information systems and finance to guide our analysis and practitioner recommendations. The cases illustrate how technologies are implicated in multijurisdictional challenges and regulatory conflicts at each end of the operational risk spectrum. We find that compliance analytics are both shaping and reporting regulatory matters yet often firms may have difficulties in recruiting individuals with relevant but diverse skill sets. The cases also underscore the increasing need for financial organizations to adopt robust information governance policies and processes to ease future remediation efforts.

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 This thesis focuses on social media adoption by the Australian banks as method of communication. It proposes a ‘technology independent’ adoption model with components that encompass sociotechnological factors governing the adoption of social media. The model can also be adapted by any business organisation and prove beneficial for future.

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In questa tesi viene affrontato il tema del tracciamento video, analizzando le principali tecniche, metodologie e strumenti per la video analytics. L'intero lavoro, è stato svolto interamente presso l'azienda BitBang, dal reperimento di informazioni e materiale utile, fino alla stesura dell'elaborato. Nella stessa azienda ho avuto modo di svolgere il tirocinio, durante il quale ho approfondito gli aspetti pratici della web e video analytics, osservando il lavoro sul campo degli specialisti del settore e acquisendo familiarità con gli strumenti di analisi dati tramite l'utilizzo delle principali piattaforme di web analytics. Per comprendere a pieno questo argomento, è stato necessario innanzitutto conoscere la web analytics di base. Saranno illustrate quindi, le metodologie classiche della web analytics, ovvero come analizzare il comportamento dei visitatori nelle pagine web con le metriche più adatte in base alle diverse tipologie di business, fino ad arrivare alla nuova tecnica di tracciamento eventi. Questa nasce subito dopo la diffusione nelle pagine dei contenuti multimediali, i quali hanno portato a un cambiamento nelle modalità di navigazione degli utenti e, di conseguenza, all'esigenza di tracciare le nuove azioni generate su essi, per avere un quadro completo dell'esperienza dei visitatori sul sito. Non sono più sufficienti i dati ottenuti con i tradizionali metodi della web analytics, ma è necessario integrarla con tecniche nuove, indispensabili se si vuole ottenere una panoramica a 360 gradi di tutto ciò che succede sul sito. Da qui viene introdotto il tracciamento video, chiamato video analytics. Verranno illustrate le principali metriche per l'analisi, e come sfruttarle al meglio in base alla tipologia di sito web e allo scopo di business per cui il video viene utilizzato. Per capire in quali modi sfruttare il video come strumento di marketing e analizzare il comportamento dei visitatori su di esso, è necessario fare prima un passo indietro, facendo una panoramica sui principali aspetti legati ad esso: dalla sua produzione, all'inserimento sulle pagine web, i player per farlo, e la diffusione attraverso i siti di social netwok e su tutti i nuovi dispositivi e le piattaforme connessi nella rete. A questo proposito viene affrontata la panoramica generale di approfondimento sugli aspetti più tecnici, dove vengono mostrate le differenze tra i formati di file e i formati video, le tecniche di trasmissione sul web, come ottimizzare l'inserimento dei contenuti sulle pagine, la descrizione dei più famosi player per l'upload, infine un breve sguardo sulla situazione attuale riguardo alla guerra tra formati video open source e proprietari sul web. La sezione finale è relativa alla parte più pratica e sperimentale del lavoro. Nel capitolo 7 verranno descritte le principali funzionalità di due piattaforme di web analytics tra le più utilizzate, una gratuita, Google Analytics e una a pagamento, Omniture SyteCatalyst, con particolare attenzione alle metriche per il tracciamento video, e le differenze tra i due prodotti. Inoltre, mi è sembrato interessante illustrare le caratteristiche di alcune piattaforme specifiche per la video analytics, analizzando le più interessanti funzionalità offerte, anche se non ho avuto modo di testare il loro funzionamento nella pratica. Nell'ultimo capitolo vengono illustrate alcune applicazioni pratiche della video analytics, che ho avuto modo di osservare durante il periodo di tirocinio e tesi in azienda. Vengono descritte in particolare le problematiche riscontrate con i prodotti utilizzati per il tracciamento, le soluzioni proposte e le questioni che ancora restano irrisolte in questo campo.

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Progettazione di un sistema di Social Intelligence e Sentiment Analysis per un'azienda del settore consumer goods

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La velocità di cambiamento che caratterizza il mercato ha posto l'attenzione di molte imprese alla Business Analysis. La raccolta, la gestione e l'analisi dei dati sta portando numerosi benefici in termini di efficienza e vantaggio competitivo. Questo è reso possibile dal supporto reale dei dati alla strategia aziendale. In questa tesi si propone un'applicazione della Business Analytics nell'ambito delle risorse umane. La valorizzazione del Capitale Intellettuale è fondamentale per il miglioramento della competitività dell'impresa, favorendo così la crescita e lo sviluppo dell'azienda. Le conoscenze e le competenze possono incidere sulla produttività, sulla capacità innovativa, sulle strategie e sulla propria reattività a comprendere le risorse e le potenzialità a disposizione e portano ad un aumento del vantaggio competitivo. Tramite la Social Network Analysis si possono studiare le relazioni aziendali per conoscere diversi aspetti della comunicazione interna nell'impresa. Uno di questi è il knowledge sharing, ovvero la condivisione della conoscenza e delle informazioni all'interno dell'organizzazione, tema di interesse nella letteratura per via delle potenzialità di crescita che derivano dal buon utilizzo di questa tecnica. L'analisi si è concentrata sulla mappatura e sullo studio del flusso di condivisione di due delle principali componenti della condivisione di conoscenza: sharing best prectices e sharing mistakes, nel caso specifico si è focalizzato lo studio sulla condivisione di miglioramenti di processo e di problematiche o errori. È stata posta una particolare attenzione anche alle relazioni informali all'interno dell'azienda, con l'obiettivo di individuare la correlazione tra i rapporti extra-professionali nel luogo di lavoro e la condivisione di informazioni e opportunità in un'impresa. L'analisi delle dinamiche comunicative e l'individuazione degli attori più centrali del flusso informativo, permettono di comprendere le opportunità di crescita e sviluppo della rete di condivisione. La valutazione delle relazioni e l’individuazione degli attori e delle connessioni chiave fornisce un quadro dettagliato della situazione all'interno dell'azienda.

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Obiettivo dii questo elaborato è cercare di dimostrare come il Web e i Social Media non sono solo dei nuovi canali di comunicazione ma costituiscono una testimonianza del radicale cambiamento che modifica la comunicazione aziendale per come è stata concepita fino ad oggi. Nascita e sviluppo del Web e passaggio dal marketing tradizionale al web marketing saranno temi centrali nella parte introduttiva. Frutto di un’analisi approfondita sarà il tema del Social media marketing, ponendo particolare attenzione ai social media più utilizzati, all’impatto che le aziende hanno con questi, i canali di comunicazione utilizzati dalle aziende e quali sono i leader del settore, quindi, le aziende, che hanno attuato ottime campagne nei social networks. In un ultima parte verranno esaminati gli strumenti attraverso i quali è possibile monitorare i comportamenti degli utenti, come ascoltarli nei social media per entrare in relazione con loro e misurare i risultati delle attività di comunicazione (Web analytics, Social media monitoring); verranno inoltre analizzati gli aspetti per una buona strategia di comunicazione aziendale nel web quindi dando uno sguardo ad un buon piano di comunicazione e alla web & brand reputation.

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Teaching is a dynamic activity. It can be very effective, if its impact is constantly monitored and adjusted to the demands of changing social contexts and needs of learners. This implies that teachers need to be aware about teaching and learning processes. Moreover, they should constantly question their didactical methods and the learning resources, which they provide to their students. They should reflect if their actions are suitable, and they should regulate their teaching, e.g., by updating learning materials based on new knowledge about learners, or by motivating learners to engage in further learning activities. In the last years, a rising interest in ‘learning analytics’ is observable. This interest is motivated by the availability of massive amounts of educational data. Also, the continuously increasing processing power, and a strong motivation for discovering new information from these pools of educational data, is pushing further developments within the learning analytics research field. Learning analytics could be a method for reflective teaching practice that enables and guides teachers to investigate and evaluate their work in future learning scenarios. However, this potentially positive impact has not yet been sufficiently verified by learning analytics research. Another method that pursues these goals is ‘action research’. Learning analytics promises to initiate action research processes because it facilitates awareness, reflection and regulation of teaching activities analogous to action research. Therefore, this thesis joins both concepts, in order to improve the design of learning analytics tools. Central research question of this thesis are: What are the dimensions of learning analytics in relation to action research, which need to be considered when designing a learning analytics tool? How does a learning analytics dashboard impact the teachers of technology-enhanced university lectures regarding ‘awareness’, ‘reflection’ and ‘action’? Does it initiate action research? Which are central requirements for a learning analytics tool, which pursues such effects? This project followed design-based research principles, in order to answer these research questions. The main contributions are: a theoretical reference model that connects action research and learning analytics, the conceptualization and implementation of a learning analytics tool, a requirements catalogue for useful and usable learning analytics design based on evaluations, a tested procedure for impact analysis, and guidelines for the introduction of learning analytics into higher education.

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Various avours of a new research field on (socio-)physical or personal analytics have emerged, with the goal of deriving semantically-rich insights from people's low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental (e.g. weather, infrastructure) and application-specific data, to better capture the interactions between users and their context, in addition to those among users. To illustrate our proposed concept of synergistic user <-> context analytics, we first provide some example use cases. Then, we present our ongoing work towards a synergistic analytics platform: a testbed, based on mobile crowdsensing and the Internet of Things (IoT), a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights.

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This paper describes the potential impact of social media and new technologies in secondary education. The case of study has been designed for the drama and theatre subject. A wide set of tools like social networks, blogs, internet, multimedia content, local press and other promotional tools are promoted to increase students’ motivation. The experiment was developed at the highschool IES Al-Satt located in Algete in the Comunidad de Madrid. The students included in the theatre group present a low academic level, 80% of them had previously repeated at least one grade, half of them come from programs for students with learning difficulties and were at risk of social exclusion. This action is supported by higher and secondary education professors and teachers who look forward to implanting networked media technologies as new tools to improve the academic results and the degree of involvement of students. The results of the experiment have been excellent, based on satisfactory opinions obtained from a survey answered by students at the end of the course, and also revealed by the analytics taken from different social networks. This project is a pioneer in the introduction and usage of new technologies in secondary high-schools in Spain.