Las redes sociales como medio de interacción estudiante-profesor: uso de Twitter para la resolución de problemas

Con objeto de encontrar nuevas metodologías que contribuyan a la formación de los estudiantes, se planteó una actividad grupal mediante el uso de la red social Twitter. El objetivo final fue fomentar la interacción, planteando una pseudo-competición donde cada participante pudiera utilizar la información generada por el resto de participantes. La actividad se realizó con alumnos de la asignatura "Farmacología" del Grado en Óptica y Optometría. Se propuso a los estudiantes la resolución de un caso clínico real publicado en una revista científica internacional, sobre un problema relacionado con el uso de fármacos en patologías oculares. Para la resolución se facilitaron una serie de pistas secuencialmente y separadas en el tiempo. Los alumnos pudieron participar proponiendo una solución al caso o planteando preguntas para su resolución. Tanto la participación activa como el planteamiento de preguntas pertinentes para la resolución del caso y su resolución implicaron una bonificación en la nota final de la asignatura. Un 60% de los alumnos matriculados participaron en la actividad. El profesorado implicado valora de forma positiva el resultado.

ElectionMap: una representación geolocalizada de intenciones de voto hacia partidos políticos sobre la base de comentarios de usuarios de Twitter

ElectionMap es una aplicación web que realiza un seguimiento a los comentarios publicados en Twitter en relación a entidades que refieren a partidos políticos. Las opiniones de los usuarios sobre estas entidades son clasificadas según su valoración y posteriormente representadas en un mapa geográfico para conocer la aceptación social sobre agrupaciones políticas en las distintas regiones de la geografía española.

Obama in Cuba: polarização e participação ativa no twitter

A histórica visita do presidente americano Barack Obama a Cuba, esta semana, impulsionou a presença dos cubanos nas redes sociais – e o debate sobre antigas questões políticas envolvendo a ilha e a difícil relação com os Estados Unidos, que apresenta contornos de conciliação, finalmente, após mais de cinco décadas. A Diretoria de Análise de Políticas Públicas (FGV/DAPP) identificou das 08h de segunda-feira, 21 de março, às 11h de terça (22) mais de 1,6 milhão de menções no Twitter sobre o assunto no mundo inteiro, sendo 800 mil entre as 10h e as 18h de segunda (horários de Brasília), quando Obama iniciou um passeio por Havana e fez uma coletiva de imprensa com o presidente cubano, Raúl Castro.

AGENDA- SETTING NO TWITTER: A CAMPANHA ELEITORAL NO MUNICÍPIO DE SÃO PAULO EM 2012

O objetivo desse trabalho é identificar uma hipótese analisada há 40 anos em uma nova forma de comunicação. A proposta é buscar a comprovação do agenda-setting, ou agendamento, no Twitter durante a eleição para a Prefeitura de São Paulo no ano de 2012. Para isso, recorremos a três portais de notícias que nos serviram como laboratório de fontes , que nos pautavam na busca pela repercussão dessas notícias na Internet. A partir da definição de alguns termos que acompanharam os três principais candidatos à prefeitura de SP, partimos para uma procura por esses termos no Twitter, através da ferramenta The Archivist . Os termos foram divididos em positivos , negativos e neutros , para identificarmos qual o tipo de conteúdo era mais repercutido. Os resultados da pesquisa identificaram uma maior repercussão de termos que representavam atributos negativos dos candidatos, analisando o agendamento dos portais de notícias como uma forma de reforço desses atributos negativos, comprovando a agenda da contrapropaganda política no Twitter.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

The representation of food in social media forum Twitter

Social Media is becoming an increasingly important part of people’s lives and is being used increasingly in the food and agriculture sector. This paper considers the extent to which each section of the food supply chain is represented in Twitter and use the hashtag #food. We looked at the 20 most popular words for each part of the supply chain by categorising 5000 randomly selected tweets to different sections of the food chain and then analysing each category. We sorted the users by those who tweeted most frequently and categorised their position in the food supply chain. Finally to consider the indegree of influence, we took the top 100 tweeters from the previous list and consider what following these users have. From this we found that consumers are the most represented area of the food chain, and logistics is the least represented. Consumers had 51.50% of the users and 87.42% of the top words tweeted from that part of the food chain. We found little evidence of logistics representation for either tweets or users (0.84% and 0.35% respectively). The top users were found to follow a high percentage of their own followers with most having over 70% the same. This research will bring greater understanding of how people perceive the food sector and how Twitter can be used within this sector.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Automatic labelling of topic models learned from Twitter by summarisation

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA. © 2014 Association for Computational Linguistics.

Nerding out on Twitter:fun, patriotism and #curiosity

This paper presents an analysis of tweets collected over six days before, during and after the landing of the Mars Science Laboratory, known as Curiosity, in the Gale Crater on the 6th of August 2012. A sociological application of web science is demonstrated by use of parallel coordinate visualization as part of a mixed methods study. The results show strong, predominantly positive, international interest in the event. Scientific details dominated the stream, but, following the successful landing, other themes emerged such as fun, and national pride.

Public scientific communication on Twitter:visual analytic approach

Purpose - The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics. Design/methodology/approach - The high-dimensional visualisation approach was applied to three scientific topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics. Findings - The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter. Research limitations/implications - Three topics are studied and these illustrate a range of frames, but results may not be representative of all scientific topics. Social implications - Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large. Originality/value - This study differs from standard approaches to the analysis of microblog data, which tend to focus on machine driven analysis large-scale datasets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.

Contextual semantics for sentiment analysis of Twitter

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words' sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.