Event Detection in Twitter Using Multi Timing Chained Windows

Twitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery using different data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW defines several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the effectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter

Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop

We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment. Previous computational studies of job-related phenomena have used corpora collected from workplace social media that are hosted internally by the employers, and so lacks independence from latent job-related coercion and the broader context that an open domain, general-purpose medium such as Twitter provides. Our new corpus promises to be a benchmark for the extraction of job-related topics and advanced analysis and modeling, and can potentially benefit a wide range of research communities in the future

Library Technologies for Boutique Services

In this chapter I examine the latest Library technologies at the time of writing (Summer 2011) and test them against the central tenets of the boutique library concept to see if they compliment or contradict each other. I draw on two specific practical case studies from my own experience at my former employer, Royal Holloway, University of London Library Services (RHULLS), and also illustrate how easy it is now to set-up a boutique Library service from scratch thanks to web technologies. My focus is very much on practical and pragmatic practitioner experience with the odd discussion on the future of technology included, in the hope that this book delivers both immediate value and insight to the reader and a record of the current thinking about UK academic library systems

An Experiment of Game Promotion and Selling Using Twitter

— The combination of the internet, social media and mobile phones makes the social mobile game is becoming a huge market with high growth rates from year to year. This trend is attract the game developers/publisher vying to enter this game market including in Indonesia. In other hand, Twitter as one of social media has a major influence on consumer purchase decisions especially in social mobile games. Consumer seeking recommendation about game that they want to download based on their friend recommendation and content that their consume in social media before visit online store. As for Indonesia game developers most of their marketing activities were more to game gathering or events, there is little that effectively use social media as marketing channel. Social media adoption including twitter in Indonesia game developer is at stage of connectivity and proff of company existance. The purpose of this research is to know does using twitter as social media marketing have effect to influence consumer and download mobile game. In this research, experiment methodology was employed. Experiment was choosed because to have real insight about the effect of twitter as social media marketing in building games relationship with consumer and increase the number of game download. Stack The Stuff, game from PT. Nightspade was choosed as research object. The implementation using OASIS frawework as guidance. The results from the experiments in this research measured using Social Model Exposure-Engagement-Influence-Action from Don Bartholomew.Twitter as media marketing executed by carrying experiment 1 (15 August 2012 - 15 September 2012) with buzzing methods first, after it finish, followed by experiment 2 (22 September - 22 October 2012) with tweeting and offering method. Then, both experiment results compared to know which the better Twitter marketing method. The measurement using several tools, namely TweetLevel, Sprout Social, and downloads data. With confidence level 95%, our results suggested that twitter as media marketing with buzzing method have effect to increase game download and tweeting and offering method have effect to increase product engagement and influence in Twitter. Furthermore, in the end of research, there are recommendations to implement twitter as social media marketing for small-middle sized company like Indonesia game developer

Merging sport and drinking cultures through social media

The sport-alcohol-social media triumvirate presents a significant emerging issue in the fight against alcohol-related harm. This report identifies and explores how alcohol brands are using social media to connect sport’s identity, culture and camaraderie with alcohol consumption. It also reveals the main strategies undertaken by alcohol companies to achieve interaction and social activation with consumers. Purpose While considerable research has explored the public health implications of traditional alcohol advertising and sponsorship in sport, less is known about how alcohol brands interact with consumers through social media. The aim of this research was to identify and evaluate the sport-linked social media strategies employed by alcohol brands engaged in sport sponsorship. These findings provide initial data to shape discussion around harm reduction as well as recommendations for future research in relation to understanding how these strategies influence consumer attitudes and behaviours. Method This study explored sport-linked alcohol communication appearing on the most frequently used social media platforms including Facebook, YouTube and Twitter. ‘Sport-linked alcohol communication’ was considered to be any marketing communication text which referenced a specific sport or sporting organisation in its message content, or any image depicting an alcohol-related product or brand in a sport context. The focus was primarily on the major alcohol brands sponsoring the Australian Football League (AFL), the National Rugby League (NRL), and Australian Cricket during the latter part of 2013 and throughout much of 2014. The study was conducted during the active seasons of each sport, paying particular attention to the associated marketing and promotional activity during key events such as finals. The unit of analysis comprised the content of the relevant social network sites and the coding units were individual posts and images. A content analysis was used to elicit themes and images from the online text. In total six beer brands, eight wine brands and three spirit brands were considered to be involved in sport sponsorship across the sports considered, although not all of these brands engaged in social media activities to leverage their association

Extracting keywords from tweets

Nos últimos anos, uma enorme quantidade de informações foi disponibilizada na Internet. As redes sociais estão entre as que mais contribuem para esse aumento no volume de dados. O Twitter, em particular, abriu o caminho, enquanto plataforma social, para que pessoas e organizações possam interagir entre si, gerando grandes volumes de dados a partir dos quais é possível extrair informação útil. Uma tal quantidade de dados, permitirá por exemplo, revelar-se importante se e quando, vários indivíduos relatarem sintomas de doença ao mesmo tempo e no mesmo lugar. Processar automaticamente um tal volume de informações e obter a partir dele conhecimento útil, torna-se, no entanto, uma tarefa impossível para qualquer ser humano. Os extratores de palavras-chave surgem neste contexto como uma ferramenta valiosa que visa facilitar este trabalho, ao permitir, de uma forma rápida, ter acesso a um conjunto de termos caracterizadores do documento. Neste trabalho, tentamos contribuir para um melhor entendimento deste problema, avaliando a eficácia do YAKE (um algoritmo de extração de palavras-chave não supervisionado) em cima de um conjunto de tweets, um tipo de texto, caracterizado não só pelo seu reduzido tamanho, mas também pela sua natureza não estruturada. Embora os extratores de palavras-chave tenham sido amplamente aplicados a textos genéricos, como a relatórios, artigos, entre outros, a sua aplicabilidade em tweets é escassa e até ao momento não foi disponibilizado formalmente nenhum conjunto de dados. Neste trabalho e por forma a contornar esse problema optámos por desenvolver e tornar disponível uma nova coleção de dados, um importante contributo para que a comunidade científica promova novas soluções neste domínio. O KWTweet foi anotado por 15 anotadores e resultou em 7736 tweets anotados. Com base nesta informação, pudemos posteriormente avaliar a eficácia do YAKE! contra 9 baselines de extração de palavra-chave não supervisionados (TextRank, KP-Miner, SingleRank, PositionRank, TopicPageRank, MultipartiteRank, TopicRank, Rake e TF.IDF). Os resultados obtidos demonstram que o YAKE! tem um desempenho superior quando comparado com os seus competidores, provando-se assim a sua eficácia neste tipo de textos. Por fim, disponibilizamos uma demo que visa demonstrar o funcionamento do YAKE! Nesta plataforma web, os utilizadores têm a possibilidade de fazer uma pesquisa por utilizador ou hashtag e dessa forma obter as palavras chave mais relevantes através de uma nuvem de palavra