Mikroblogintza tekniken erabilera programazioan sortzen diren zalantzak ebazteko

Gaur egun Twitter moduko mikroblogintza aplikazioak zalantzak argitzeko gero eta gehiago erabiltzen dira. Erabiltzaileak laguntza eskaera adierazi eta sare sozialaren bidez bere kontaktuei helarazi egiten zaie. Erabiltzaileak galdera eta erantzunen kudeaketa sare sozial nahaste baten barruan egin beharko du. Proiektuaren helburu nagusia mikroblogintza plataformetako kanala programazio lengoaien IDEen barruan bateratzea da, modu honetan laguntza eskatzeko prozesua IDEetan bertan kudeatzeko.

A linguagem no Twitter: um enfoque sistêmico-funcional

O objetivo deste trabalho é investigar as características da linguagem no Twitter, focalizando (i) seu propósito comunicativo, (ii) seus participantes discursivos e (iii) suas relações interpessoais. Por acreditar que a linguagem é um recurso sistemático e que somente através dela expressamos significados em determinados contextos, encontramos na Linguística Sistêmico-Funcional (LSF) uma base teórica que se encaixa à pesquisa. Para Halliday(1994), a linguística é o estudo de como as pessoas negociam sentido através do uso da linguagem. Assim, encontramos no Twitter, um corpus diversificado que reforça ainda mais a teoria da LSF, quando afirma sermos nós, falantes da língua, os únicos responsáveis por nossas escolhas lexicais, tendo consciência de como e onde, contextualmente falando, podemos aplicar em uma atividade linguística em que estivermos engajados. O material de pesquisa foi constituído mediante a coleta inicial de 671 comentários postados no Twitter em 2010. Dados obtidos a partir da análise desta coleta confirmam o argumento de Crystal (2011), de que a expressão de opinião é o principal propósito comunicativo das mensagens postadas no microblogging. Assim, após recortes no corpus para coleta exclusivamente de opiniões, 201 tuítes resultantes de duas coletas realizadas em datas e situações diferentes foram analisados: uma, após notícia de agressão a uma professora; a segunda, momentos antes e durante a Copa Mundial de 2010. Os resultados apontam diferenças entre as amostras, principalmente em função de aspectos do contexto de situação: pois embora o tom seja de indignação nas amostras com tuítes opinativos, apenas na amostra futebol há tentativa de se orientar a ação do outro. Quanto às relações interpessoais, foram identificadas marcas de interação face a face nas duas amostras, mas apenas na amostra futebol identificou-se uso de linguagem de baixo calão. Finalmente, em relação às características gerais do Twitter, observa-se o uso de linguagem reduzida na forma de caracteres emotivos ou de abreviações, o uso de interjeições e pontos de exclamação. Observou-se ainda o uso recorrente de léxico valorativo, de ironia e de perguntas retóricas para expressão de indignação, mas estes traços parecem ser afetados por aspectos do contexto de situação, mais do que por características do Twitter

Finding Relevant Tweets

When a user of a microblogging site authors a microblog
post or browses through a microblog post, it provides cues as to what
topic she is interested in at that point in time. Example-based search
that retrieves similar tweets given one exemplary tweet, such as the one
just authored, can help provide the user with relevant content. We investigate
various components of microblog posts, such as the associated
timestamp, author’s social network, and the content of the post, and
develop approaches that harness such factors in finding relevant tweets
given a query tweet. An empirical analysis of such techniques on real
world twitter-data is then presented to quantify the utility of the various
factors in assessing tweet relevance. We observe that content-wise similar
tweets that also contain extra information not already present in the
query, are perceived as useful. We then develop a composite technique
that combines the various approaches by scoring tweets using a dynamic
query-specific linear combination of separate techniques. An empirical
evaluation establishes the effectiveness of the composite technique, and
that it outperforms each of its constituents.

Enterprise Social Networking

Microblogging in the workplace as a functionality of Enterprise Social Networking (ESN) platforms is a relatively new phenomenon of which the use in knowledge work has not yet received much attention from research. In this cross-sectional study, I attempt to shed light on the role of microblogging in knowledge work. I identify microblogging use practices of knowledge workers on ESN platforms, and I identify its role in supporting knowledge work performance. A questionnaire is carried out among a non-representative sample of knowledge workers. The results shed light on the purposes of the microblogging messages that knowledge workers write. It also helps us find out whether microblogging supports them in performing their work. The survey is based on existing theory that supplied me with possible microblog purposes as well as theory on what the actions of knowledge workers are. The results reveal that “knowledge & news sharing”, “crowd sourcing”, “socializing & networking” and “discussion & opinion” are frequent microblog purposes. The study furthermore shows that microblogging benefits knowledge workers’ work. Microblogging seems to be a worthy addition to the existing means of communication in the workplace, and is especially useful to let knowledge, news and social contact reach a further and broader audience than it would in a situation without this social networking service.

A era de um mercado social

O crescimento e a expansão das redes sociais trouxe novas formas de interação entre os seres humanos que se repercutem na vida real. Os textos partilhados nas redes sociais e as interações resultantes de todas as atividades virtuais têm vindo a ganhar um grande impacto no quotidiano da sociedade e no âmbito económico e financeiro, as redes sociais tem sido alvo de diversos estudos, particularmente em termos de previsão e descrição do mercado acionista (Zhang, Fuehres, & Gloor, 2011) (Bollen, Mao & Zheng, 2010). Nesta investigação percebemos se o sentimento do Twitter, rede social de microblogging, se relaciona diretamente com o mercado acionista, querendo assim compreender qual o impacto das redes sociais no mercado financeiro. Tentámos assim relacionar duas dimensões, social e financeira, de forma a conseguirmos compreender de que forma poderemos utilizar os valores de uma para prever a outra. É um tópico especialmente interessante para empresas e investidores na medida em que se tenta compreender se o que se diz de determinada empresa no Twitter pode ter relação com o valor de mercado dessa empresa. Usámos duas técnicas de análise de sentimentos, uma de comparação léxica de palavras e outra de machine learning para compreender qual das duas tinha uma melhor precisão na classificação dos tweets em três atributos, positivo, negativo ou neutro. O modelo de machine learning foi o modelo escolhido e relacionámos esses dados com os dados do mercado acionista através de um teste de causalidade de Granger. Descobrimos que para certas empresas existe uma relação entre as duas variáveis, sentimento do Twitter e alteração da posição da ação entre dois períodos de tempo no mercado acionista, esta última variável estando dependente da dimensão temporal em que agrupamos o nosso sentimento do Twitter. Este estudo pretendeu assim dar seguimento ao trabalho desenvolvido por Bollen, Mao e Zheng (2010) que descobriram que uma dimensão de sentimento (calma) consegue ser usada para prever a direção das ações do mercado acionista, apesar de terem rejeitado que o sentimento geral (positivo, negativo ou neutro) não se relacionava de modo global com o mercado acionista. No seu trabalho compararam o sentimento de todos os tweets de um determinado período sem exclusão com o índice geral de ações no mercado enquanto a metodologia adotada nesta investigação foi realizada por empresa e apenas nos interessaram tweets que se relacionavam com aquela empresa em específico. Com esta diferença obtemos resultados diferentes e certas empresas demonstravam que existia relação entre várias combinações, principalmente para empresas tecnológicas. Testamos o agrupamento do sentimento do Twitter em 3 minutos, 1 hora e 1 dia, sendo que certas empresas só demonstravam relação quando aumentávamos a nossa dimensão temporal. Isto leva-nos a querer que o sentimento geral da empresa, e se a mesma for uma empresa tecnológica, está ligado ao mercado acionista estando condicionada esta relação à dimensão temporal que possamos estar a analisar.

Twitter y la enseñanza del español como segunda lengua

Se describe en primer lugar el servicio de microblogging Twitter para ahondar a continuación en las posibilidades que ofrece esta red social en la enseñanza del español como lengua extranjera y los beneficios que puede aportar a los docentes tanto en la construcción de entornos personales de aprendizaje como en la incorporación de la herramienta al aula de Español como Lengua Extranjera (ELE).

Servicios de microblogs en la enseñanza secundaria.

Resumen tomado de la publicación

El uso de Twitter como herramienta para la ense??anza universitaria en el ??mbito de las ciencias sociales : un estudio de caso desde la Historia Econ??mica

Resumen basado en el de la publicaci??n

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.

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.

Big social data and political sentiment: the tweet stream during the UK General Election 2015 campaign

The General Election for the 56th United Kingdom Parliament was held on 7 May 2015. Tweets related to UK politics, not only those with the specific hashtag ”#GE2015”, have been collected in the period between March 1 and May 31, 2015. The resulting dataset contains over 28 million tweets for a total of 118 GB in uncompressed format or 15 GB in compressed format. This study describes the method that was used to collect the tweets and presents some analysis, including a political sentiment index, and outlines interesting research directions on Big Social Data based on Twitter microblogging.

Trendtv : uma arquitetura para mudança automática de canais de TV baseada em redes sociais virtuais com graus de amizade e suporte a múltiplos dispositivos no cenário da TV digital brasileira

Due to the large amount of television content, which emerged from the Digital TV, viewers are facing a new challenge, how to find interesting content intuitively and efficiently. The Personalized Electronic Programming Guides (pEPG) arise as an answer to this complex challenge. We propose TrendTV a layered architecture that allows the formation of social networks among viewers of Interactive Digital TV based on online microblogging. Associated with a pEPG, this social network allows the viewer to perform content filtering on a particular subject from the indications made by other viewers of his network. Allowing the viewer to create his own indications for a particular content when it is displayed, or to analyze the importance of a particular program online, based on these indications. This allows any user to perform filtering on content and generate or exchange information with other users in a flexible and transparent way, using several different devices (TVs, Smartphones, Tablets or PCs). Moreover, this architecture defines a mechanism to perform the automatic exchange of channels based on the best program that is showing at the moment, suggesting new components to be added to the middleware of the Brazilian Digital TV System (Ginga). The result is a constructed and dynamic database containing the classification of several TV programs as well as an application to automatically switch to the best channel of the moment