Lectura i Twitter : la tert??lia @llegimipiulem : an??lisi per a una proposta d'aula

Resumen basado en el de la publicaci??n

Aprender a usar twitter y usar twitter para aprender

Resumen basado en el de la publicaci??n

Opinión pública 2.0 en el entorno de Twitter respecto al proyecto de Ley Orgánica de Comunicación en el Ecuador

La investigación Opinión pública 2.0 en el entorno de Twitter respecto al proyecto de Ley Orgánica de Comunicación en Ecuador realiza una aproximación desde el análisis de contenido a los mensajes de Twitter respecto al asunto de la Ley en cuestión. Muestra la diversidad de opiniones de una comunidad dialogante que trata de aportar con ideas a la discusión política de la Ley en el seno de la Asamblea Nacional. Los datos muestran que existe un imaginario de censura y coartamiento a las libertades pero al mismo tiempo la necesidad de impostar una ley que cambie el panorama comunicacional del Ecuador, acorde con las exigencias del mandato popular. Aunque existe esta dicotomía, el trabajo muestra que a partir de estas evidencias es necesario pensar la noción de comunidad social y comunidad democrática a partir de la idea de la conversación, base de lo que acontece en Twitter. El autor asimismo plantea la necesidad de pensar una nueva formación social, cual es la opinión pública 2.0 diferente a las clásicas convenciones que se tienen de esta.

Información, periodismo y tecnología: uso de Twitter en el periodismo ecuatoriano

Cada día se incrementa el uso de las tecnologías de información y comunicación en Ecuador. Los periodistas aprovechan las herramientas digitales para obtener o publicar información. La inmediatez que estas suscitan ha dado un giro interesante al momento de informar. En ese contexto, fue importante dar paso a la investigación titulada “Información, periodismo y tecnología: uso de Twitter en el periodismo ecuatoriano”, con objeto de determinar el tipo de información que los periodistas publican en Twitter. De la misma manera, con esta investigación se pudo analizar si estas prácticas pueden devenir en un nuevo género periodístico, además de identificar el tipo de tweets que son noticia. Twitter ha llamado la atención de los periodistas y medios de comunicación por la rapidez de la información; muchas noticias se han conocido primero a través de este microblogging, pues la mayoría de las publicaciones son leads periodísticos con altos contenidos noticiosos. El periodismo digital ha captado a profesionales y audiencias, por la facilidad de leer la noticia y los hechos del momento. Un mapa de medios de comunicación y periodistas en Twitter permitió conocer qué tan usada es esta plataforma digital. El principal punto de atención de este trabajo fue la Ley Orgánica de Comunicación, con el hashtag: #leydecomunicacion. Tomando en cuenta a este se siguió las cuentas de 5 periodistas legislativos, quienes postearon los momentos previos a su aprobación, desde la votación y las implicaciones del debate legislativo. De acuerdo a lo dicho, Twitter es para los periodistas una herramienta digital importante para su trabajo diario. La corta pero precisa información permite incluso ampliar algún tema y desarrollar una investigación periodística. En este trabajo comprobamos finalmente que lo desarrollado en Twitter no puede ser considerado como un género periodístico aunque sí encaja dentro de los microgéneros desarrollados en internet donde destacan por la brevedad de la información pero también por la multimedialidad que permite este microblogging.

What people study when they study Twitter: classifying Twitter related academic papers

PURPOSE: Since its introduction in 2006, messages posted to the microblogging system Twitter have provided a rich dataset for researchers, leading to the publication of over a thousand academic papers. This paper aims to identify this published work and to classify it in order to understand Twitter based research. DESIGN/METHODOLOGY/APPROACH: Firstly the papers on Twitter were identified. Secondly, following a review of the literature, a classification of the dimensions of microblogging research was established. Thirdly, papers were qualitatively classified using open coded content analysis, based on the paper’s title and abstract, in order to analyze method, subject, and approach. FINDINGS: The majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users. A variety of methodological approaches are used across a range of identified domains. RESEARCH LIMITATIONS/IMPLICATIONS: This work reviewed the abstracts of all papers available via database search on the term “Twitter” and this has two major implications: 1) the full papers are not considered and so works may be misclassified if their abstract is not clear, 2) publications not indexed by the databases, such as book chapters, are not included. ORIGINALITY/VALUE: To date there has not been an overarching study to look at the methods and purpose of those using Twitter as a research subject. Our major contribution is to scope out papers published on Twitter until the close of 2011. The classification derived here will provide a framework within which researchers studying Twitter related topics will be able to position and ground their work

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper.

How Twitter is studied in the medical professions: a classification of Twitter papers indexed in PubMed

Background: Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. // Objective: This paper aims to identify published work relating to Twitter indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Limiting the study to papers indexed by PubMed ensures the work provides a reproducible benchmark. // Methods: Papers, indexed by PubMed, on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain and aspect. // Results: As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focussed on Twitter (the others referring to it tangentially). The early Twitter focussed papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. // Conclusions: This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used five dimensions to categorise published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research relating to Twitter in the area of medicine and beyond, can position and ground their work.

Bigger data for Big Data: from Twitter to brain-computer interface

We are sympathetic with Bentley et al’s attempt to encompass the wisdom of crowds in a generative model, but posit that success at using Big Data will include more sensitive measurements, more and more varied sources of information, as well as build from the indirect information available through technology, from ancillary technical features to data from brain-computer interface.

Rule Type Identification using TRCM for trend analysis in Twitter

This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.

Conversations on Twitter: structure, pace, balance

Twitter is both a micro-blogging service and a platform for public conversation. Direct conversation is facilitated in Twitter through the use of @’s (mentions) and replies. While the conversational element of Twitter is of particular interest to the marketing sector, relatively few data-mining studies have focused on this area. We analyse conversations associated with reciprocated mentions that take place in a data-set consisting of approximately 4 million tweets collected over a period of 28 days that contain at least one mention. We ignore tweet content and instead use the mention network structure and its dynamical properties to identify and characterise Twitter conversations between pairs of users and within larger groups. We consider conversational balance, meaning the fraction of content contributed by each party. The goal of this work is to draw out some of the mechanisms driving conversation in Twitter, with the potential aim of developing conversational models.

Extraction of unexpected rules from Twitter hashtags and its application to sport events

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

Of Facebook, Twitter and Social Networking Sites

For those who have read even one of my musings, it will come as no surprise that I find Facebook, Twitter, social networking sites (SNS), and the rest of Webology less than inspiring. If you had read nothing other than the screed I blathered about Google a few columns back, you’d know that I find all this talk about the Web replacing libraries more than a little silly; I find it downright idiotic. Still, one must keep an open mind.

Twitter: microblog e rede social

Trata-se de uma pesquisa exploratória sobre o Twitter, um tipo rede social na Internet em formato de microblog. A questão central foi compreender a popularização do serviço, sobretudo no Brasil, e como este pode ser utilizado como ferramenta de comunicação pessoal ou, mesmo, corporativa. Este estudo preliminar foi desenvolvido por meio de pesquisa documental sobre redes sociais na Internet e pesquisa exploratória, com base em artigos publicados sobre o Twitter. A principal constatação é que a utilização das redes sociais como o Twitter para difusão de informações, sejam pessoais, sejam corporativas, utilizando-se de perfis reais e institucionais em sua divulgação, gera maior credibilidade das ações desenvolvidas, bem como estas são mais rápidas e podem ser acessadas via browser ou aplicativos que rodam em outros gadgets, como celulares ou smartphones.

Redes Sociais Virtuais: o uso do Twitter pelas universidades públicas

RAMOS, Rogéria Campos. Redes sociais virtuais: o uso do twitter pelas universidades públicas. 2012. 119f. Dissertação (Mestrado em Comunicação)-Universidade Municipal de São Caetano do Sul, São Caetano do Sul, 2012.