FIFA World Cup 2014 on Twitter and Facebook: more from less or less from more?

The last FIFA World Cup in Brazil presented the ambition of a global event, referring both to the number of audiences and the number of platforms involved in its coverage. Taking the advantages promoted by social media platforms and mobile technologies, media companies had the opportunity to try new strategies in a truly ambient information reality. Taking into account the development of Twitter, assuming itself as an informative and mobile platform, it becomes necessary to promote a further reflection about the impact of social media in the field of journalism. Applying this research to sport we follow other analyses made about political issues. In this study, we have analyzed 3195 post on Twitter and 665 on Facebook, made by media outlets from seven different countries, during the coverage of 32 matches of the World Cup in Brazil, in June and July 2014.O último campeonato do Mundo FIFA, que se realizou no Brasil, apresentou a ambição de um evento global, tanto ao nível das audiências como nas plataformas utilizadas na sua cobertura. Aproveitando as vantagens promovidas pelas plataformas de media social e os periféricos móveis, as empresas mediáticas tiveram a oportunidade de experimentar novas estratégias, numa realidade marcada pela ambient information. Tendo em conta o desenvolvimento do Twitter, que se assume cada vez mais como uma plataforma informativa móvel, é necessário refletir sobre o impato das media sociais no campo do jornalismo. Aplicando esta pesquisa ao desporto, seguimos outras análises feitas em questões relacionadas com a política. Neste estudo, analisámos 3195 posts no Twitter e 665 no Facebook feitos por meios de sete países diferentes, durante a cobertura de 32 partidas do Campeonato do Mundo do Brasil, em junho e julho de 2014

Sentiment analysis on Twitter data using machine learning

In the world of social media people are more responsive towards product or certain events that are currently occurring. This response given by the user is in form of raw textual data (Semi Structured Data) in different languages and terms, which contains noise in data as well as critical information that encourage the analyst to discover knowledge and pattern from the dataset available. This is useful for decision making and taking strategic decision for the future market. To discover this unknown information from the linguistic data Natural Language Processing (NLP) and Data Mining techniques are most focused research terms used for sentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment of the people throughout the world using machine learning techniques. Here the data set available for research is from Twitter for world cup Soccer 2014, held in Brazil. During this period, many people had given their opinion, emotion and attitude about the game, promotion, players. By filtering and analyzing the data using natural language processing techniques, and sentiment polarity has been calculated based on the emotion word detected in the user tweets. The data set is normalized to be used by machine learning algorithm and prepared using natural language processing techniques like Word Tokenization, Stemming and lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK), which is openly available for academic as well as for research purpose. Derived algorithm extracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a sentence that has a meaning in current context, and is assigned sentiment polarity using ‘SentWordNet’ Dictionary or using lexicon based method. The resultant polarity assigned is further analyzed using Naïve Bayes and SVM (support vector Machine) machine learning algorithm and visualized data on WEKA platform. Finally, the goal is to compare both the results of implementation and prove the best approach for sentiment analysis on social media for semi structured data.Master of Science (MSc) in Computational Science

The future of jihad: what next for ISIL and al-Qaeda?

This report examines what the rise of ISIL means for al-Qaeda and how will it react. Overview ISIL is a real threat and must be targeted, but al-Qaeda shouldn’t be forgotten. Indeed, al-Qaeda should remain a key focus for international counterterrorism efforts. It’s a resilient and resolute terrorist organisation, but it’s also weaker than it’s been for many years. We should use this brief opportunity to dismantle the organisation completely. The report examines what the rise of ISIL means for al-Qaeda and how will it react. How will al-Qaeda seek to regain the oxygen of publicity that’s central to terrorist organisations if they’re to recruit, grow and, ultimately, challenge their enemies? Does the rise of ISIL signal the end of al-Qaeda or might al-Qaeda merge with ISIL, confront it head on or take some other course of action? The authors explore four alternative futures for al-Qaeda and ISIL and conclude that a worrying scenario of ‘one-upmanship’ is likely to take place between the two organisations in which al-Qaeda pursues a campaign of international attacks in order to regain the limelight

Multimedia big data computing for trend detection

The Big data analysis has becoming increasingly more relevant for the enterprises because the efficient handling of information represents a unique competitive advantage, being its application so diverse as the nature of the data. Ejm. Fraud detection, advertising strategies, web traffic m onitoring, etc. Apache Spark is a engine for large - scale data processing, intended to be a drop in replacement for Hadoop MapReduce providing the benefit of improved performance; the main goal of this project is proof the capabilities of this system, throu gh the development and implementation of a distributed pipeline for processing and indexing at high speed and real - time multimedia data streams generated by social networks and detect trends in these, using for this purpose the Spark related projects and l ibraries: Spark Streaming and Spark MLlib. To verify the effectiveness of the algorithm, different benchmarks (with different configurations) will be performed, these results will be analyzed

Tweets Are Not Created Equal:investigating Twitter's client ecosystem

This article offers an investigation into the developer ecosystem of platforms drawing on the specific case of Twitter and explores how third-party clients enable different “ways of being” on Twitter. It suggests that researchers need to consider digital data as traces of distributed accomplishments between platforms, users, interfaces, and developers. The argument follows three main steps: We discuss how Twitter’s bounded openness enables and structures distributed data production through grammatization of action. We then suggest ways to explore and qualify sources by drawing on a weeklong data set of nearly 32 million tweets, retrieved from Twitter’s 1% random sample. We explore how clients show considerable differences in tweet characteristics and degrees of automation, and outline methodological steps to deploy the source variable to further investigate the heterogeneous practices common metrics risk flattening into singular counts. We conclude by returning to the question about the measures of the medium, suggesting how they might be revisited in the context of increasingly distributed platform ecosystems, and how platform data challenge key ideas of digital methods research

Symbolisen konvergenssin teoria Twitter-tutkimuksen välineenä:tapaus #nokia

Tarinat ovat osa sosiaalisia todellisuuksia, jotka rakentuvat ihmisten välisessä vuorovaikutuksessa. Näistä tarinoista yhä useampi syntyy nykyään sosiaalisessa mediassa, koska sen merkitys ihmisten vuorovaikutukselle kasvaa jatkuvasti. Tässä artikkelissa pyrimme osoittamaan tarinoita fantasioiksi kutsuvan symbolisen konvergenssin teorian tarjoamat mahdollisuudet viestinnän tutkimukselle. Sovellamme teoriaa sosiaalisen median palvelu Twitteriin. Keskitymme niin sanottuun #nokia-keskusteluun, jota käytiin Twitterissä Nokian ja Microsoftin välisen yrityskaupan julkistamisen jälkeen syyskuussa 2013. Esittelemme teoriaa ja analysoimme keskustelua rinnakkain teorian keskeisten käsitteiden kautta. Lopuksi arvioimme teoriaa ja mahdollisuuksia hyödyntää sitä viestinnän tutkimukseen myös jatkossa.Tarinat ovat osa sosiaalisia todellisuuksia, jotka rakentuvat ihmisten välisessä vuorovaikutuksessa. Näistä tarinoista yhä useampi syntyy nykyään sosiaalisessa mediassa, koska sen merkitys ihmisten vuorovaikutukselle kasvaa jatkuvasti. Tässä artikkelissa pyrimme osoittamaan tarinoita fantasioiksi kutsuvan symbolisen konvergenssin teorian tarjoamat mahdollisuudet viestinnän tutkimukselle. Sovellamme teoriaa sosiaalisen median palvelu Twitteriin. Keskitymme niin sanottuun #nokia-keskusteluun, jota käytiin Twitterissä Nokian ja Microsoftin välisen yrityskaupan julkistamisen jälkeen syyskuussa 2013. Esittelemme teoriaa ja analysoimme keskustelua rinnakkain teorian keskeisten käsitteiden kautta. Lopuksi arvioimme teoriaa ja mahdollisuuksia hyödyntää sitä viestinnän tutkimukseen myös jatkossa