Tweeting Activity Research Articles

BSMA

The demonstration of a benchmark, named as BSMA, for Benchmarking Social Media Analytics, is introduced in this paper. BSMA is designed to benchmark data management systems supporting analytical queries over social media. It is different to existing benchmarks in that: 1) Both real-life data and a synthetic data generator are provided. The real-life dataset contains a social network of 1.6 million users, and all their tweeting and retweeting activities. The data generator can generate both social networks and synthetic timelines that follow data distributions determined by predefined parameters. 2) A set of workloads are provided. The data generator is in responsible for producing updates. A workload generator produces queries based on predefined query templates by generating query arguments online. BSMA workloads cover a large amount of queries with graph operations, temporal queries, hotspot queries, and aggregate queries. Furthermore, the argument generator is capable of sampling data items in the timeline following power-law distribution online. 3) A toolkit is provided to measure and report the performance of systems that implement the benchmark. Furthermore, a prototype system based on dataset and workloads of BSMA is also implemented. The demonstration will include two parts, i.e. the internals of data and workload generator, as well as the performance testing of reference implementations.

Spectral clustering for sensing urban land use using Twitter activity

Individuals generate vast amounts of geolocated content through the use of mobile social media applications. In this context, Twitter has become an important sensor of the interactions between individuals and their environment. Building on this idea, this paper proposes the use of geolocated tweets as a complementary source of information for urban planning applications, focusing on the characterization of land use. The proposed technique uses unsupervised learning and automatically determines land uses in urban areas by clustering geographical regions with similar tweeting activity patterns. Three case studies are presented and validated for Manhattan (NYC), London (UK) and Madrid (Spain) using Twitter activity and land use information provided by the city planning departments. Results indicate that geolocated tweets can be used as a powerful data source for urban planning applications.

Social dynamics of Twitter usage in London, Paris, and New York City

The penetration and use of social media services differs from city to city. This paper investigates the social dynamics of Twitter social media usage in three ethnically diverse cities — London, Paris, and New York City. We present a spatial analysis of Tweeting activity in the three cities, broken down by ethnicity and gender. We model the ethnic identity of Twitter users using their paired forenames and surnames. The geo–tagged Tweets provide an insight into the geography of their activity patterns across the three cities. The gender of each Twitter user is identified through classification of forenames, suggesting that, irrespective of the ethnic identity, the majority of Twitter users are male. Taken together, the results present a window on the activity patterns of different ethnic groups.

Social media: A tool to spread information: A case study analysis of Twitter conversation at the Cardiac Society of Australia & New Zealand 61st Annual Scientific Meeting 2013

The World Wide Web has changed the way in which people communicate and consume information. More importantly, this innovation has increased the speed and spread of information. There has been recent increase in the percentage of cardiovascular professionals, including journals and associations using Twitter to engage with others and exchange ideas. Evaluating the reach and impact in scientific meetings is important in promoting the use of social media. This study evaluated Twitter use during the recent 61st Annual Scientific Meeting at the Cardiac Society of Australia and New Zealand. During the Cardiac Society of Australia and New Zealand 2013 61st Annual Scientific Meeting Symplur was used to curate conversations that were publicly posted with the hashtag #CSANZ2013. The hashtag was monitored with analysis focused on the influencers, latest tweets, tweet statistics, activity comparisons, and tweet activity during the conference. Additionally, Radian6 social media listening software was used to collect data. A summary is provided. There were 669 total tweets sent from 107 unique Twitter accounts during 8th August 9 a.m. to 11th August 1 p.m. This averaged nine tweets per hour and six tweets per participant. This assisted in the sharing of ideas and disseminating the findings and conclusions from presenters at the conference with a total 1,432,573 potential impressions in Twitter users tweet streams. This analysis of Twitter conversations during a recent scientific meeting highlights the significance and place of social media within research dissemination and collaboration. Researchers and clinicians should consider using this technology to enhance timely communication of findings. The potential to engage with consumers and enhance shared decision-making should be explored further.

Correction: Quantifying Collective Attention from Tweet Stream

There was an error in the funding statement. The correct funding information is: This research is supported by the Japan Society for the Promotion of Science through the “Funding Program for World-Leading Innovative R&D on Science and Technology (FIRST Program),” initiated by the Council for Science and Technology Policy (CSTP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The legends for the Supporting Information figures were omitted. The full titles and legends for the Supporting Information Figures are as follows: Fig. S1. Annual variation in daily tweet activity. Mean tweet probability distribution in 2010 (P) and 2011 (Q). JS(P, Q)=0.0009. Fig. S2. Collective attention related to multiple events. Tweet probability distribution, and the rankings of the popularity and the enhancement of key terms in Japanese tweets on Jun 13, 2010. Each black bar in the figure denotes the time range during which tweets were considered for calculating term frequency. The red text in the tables denotes terms related to (A) an earthquake and (B) Hayabusa's return to the Earth. (C) in the figure is related to Twitter outage. The text in parentheses denotes the English translation. Fig. S3. Collective attention related to new year holidays. Tweet probability distribution, and the rankings of the popularity and the enhancement of key terms in Japanese tweets on January 1, 2011. The black bar in the figure represents the time range during which tweets were considered for calculating term frequency. The red text in the table represent terms related to new year holidays, and the text in parentheses denotes the English translation.

Quantifying Collective Attention from Tweet Stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.

FireWatch: Creative Responses to Bushfire Catastrophes

FireWatch: Creative Responses to Bushfire Catastrophes

Twitter‐based information distribution during the 2009 Red River Valley flood threat

Twitter‐based information distribution during the 2009 Red River Valley flood threat

P.5.076 Analysis of cerebrovascular andneuroprotective effects of afobazole

The social media microblogging service Twitter is gaining popularity in the field of urology as a fast and effective communication platform. We assessed volume, subject matter, influencers and content of urology related discussions on the Twitter platform using the recently proposed Urology Tag Ontology hashtag list.We queried the Symplur Signals database for tweet activity during a 1-year period. We used the Urology Tag Ontology hashtag list, comprised of 45 disease related hashtags in 9 urological subspecialties, to assess activity (numbers of tweets, users and impressions), users (geolocation, influencers) and content (words, tweet enhancements) of urology related Twitter traffic.Twitter activity during the study period included 334,642 tweets by 104,166 users with 1,397,107,305 impressions. Tweet activity varied among urological subspecialties and was largely dominated by urological oncology topics driven by #ProstateCancer. Users came from 224 countries and from all continents around the globe. Health care organizations accounted for the largest proportion of influencers (44%), followed by other individuals (22%) and physicians (13%). The top words were “prevent” (used 20,955 times), “cancer” (19,610), “follow” (19,169), “men” (19,165) and “condom” (18,425). The median (range) number of shares was 2,200 (1,414-8,854) for the top 10 links, 2,123 (1,878-2,737) for the top 10 retweets and 207 (12-438) for the top 10 photos.Twitter activity in the field of urology can be assessed using a standardized list of social media communication descriptors. The value of the Twitter communication platform is underscored by the large number of tweets and impressions in the urology space.