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What's (technically) in your tweets? | #datascience #API #twitter

What's (technically) in your tweets? | #datascience #API #twitter | e-Xploration | Scoop.it
Just because you only see 140 characters doesn't mean that Twitter isn't getting complicated behind the scenes. Here's how status objects are evolving.
luiy's insight:

.. an interesting map of what's going on behind your Twitter stream. As it turns out, there is quite a bit of data associated with not just you as a user, but also with every tweet that you post to the service.

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Hacking public-facing data visualizations at #Twitter / @philogb | #dataviz #ddj

Hacking public-facing visualizations at Twitter Nicolas Garcia Belmonte / @philogb

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Chorus Project : #Twitter #analytics tool suite | #bigdata

Chorus Project : #Twitter #analytics tool suite | #bigdata | e-Xploration | Scoop.it
Twitter data retrieval and visual analytics. Designed for social research. GUI based for easy access and fast productivity.
luiy's insight:

The Chorus package currently comprises of two distinct programs:

Tweetcatcher

Firstly, we have Chorus-TCD (TweetCatcher Desktop). Tweetcatcher allows users to sift Twitter for relevant data in two distinct ways: either by topical keywords appearing in Twitter conversation widely (i.e. semantically-driven data) or by identifying a network of Twitter users and following their daily ‘Twitter lives’ (i.e. user-driven data).

Tweetvis

Secondly, we have Chorus-TV (TweetVis), which is a visual analytic suite for facilitating both quantitative and qualitative approaches to social media data in social science. Visual analytics (VA) is an interdisciplinary computing methodology combining methods from data mining, information visualization, human-computer interaction and cognitive psychology. The VA approach is highly relevant to the aims of Chorus, enabling exploratory analysis of social media data in an intuitive and user-friendly fashion. Two main views are available within Chorus-TV. The Timeline Explorer (below) provides users an opportunity to analyse Twitter data across time and visualize the unfolding Twitter conversation according to various metrics (including tweet frequency, sentiment, semantic novelty and homogeneity, collocated words, and so on).

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#SemanadelEmprendedor - México 2014 - Gráficos de conversaciones y Ecosistema | #SNA #gephi

#SemanadelEmprendedor - México 2014 - Gráficos de conversaciones y Ecosistema | #SNA #gephi | e-Xploration | Scoop.it
luiy's insight:

En el contexto del evento #SemanadelEmprendedor 2014 en México desarrollamos y presentamos los siguiente gráficos:

 

- los actores principales que participarón en Twitter en el contexto del evento #SemanadelEmprendedor 2014

 

- los actores principales del Ecosistema Emprendedor 2014 en México.

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( #Fake ) friends with (Real) benefits?? | #sna #socialmedia

( #Fake ) friends with (Real) benefits?? | #sna #socialmedia | e-Xploration | Scoop.it
I paid $5 for 4,000 Twitter followers, and here’s what I found
luiy's insight:

The Experiment

 

At the start, I used Twitter’s API to get a list of my 2,600 existing Twitter followers. Then I set about figuring out where to buy more.

Google conveniently auto-completed my search for “buy twitter” with a number of useful suggestions, including: “buy twitter followers,” “buy twitter followers cheap” and “buy twitter followers reviews.” I was certainly not the only one searching for this.

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Four Ways of Looking at Twitter | #dataviz #SNA

Four Ways of Looking at Twitter | #dataviz #SNA | e-Xploration | Scoop.it
Data visualization is cool. It's also becoming ever more useful, as the vibrant online community of data visualizers (programmers, designers, artists, and statisticians — sometimes all in one...
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Cluster your Twitter Data with #R and #k-means | #datascience

Cluster your Twitter Data with #R and #k-means | #datascience | e-Xploration | Scoop.it

Hello everbody! Today  I want to show you how you can get deeper insights into your Twitter followers with the help of R


Via ukituki
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Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API wit Twitter’s Firehose | #datascience

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Want to be Retweeted? Large Scale Analytics on #Factors Impacting Retweet in #Twitter Network | #datascience

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What is Chat, Twitter, text messaging and instant messaging abbreviations? - Definition I #semantic #cyberculture

This is a long list of abbreviations used in e-mail and online chatting. Chat abbreviations are commonly used in e-mail, online chatting, online discussion forum postings, instant messaging, and in text messaging, especially between cell phone users.
luiy's insight:
AbbreviationMeaning<3heart404I haven't a clueA3Anyplace, anywhere, anytimeADNAny day nowAFAIKAs far as I knowAFKAway from keyboardAREAcronym-rich environmentASAPAs soon as possible
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New #Algorithm Can Spot the Bots in Your Twitter Feed | #dataviz

New #Algorithm Can Spot the Bots in Your Twitter Feed | #dataviz | e-Xploration | Scoop.it
Researchers have created an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill.

Via Pierre Levy
luiy's insight:

You know Twitter spam when you see it—but wouldn’t it be nice if you didn’t have to see it?

 

Unfortunately, email-style filters, which analyze message contents, are of little help. Due to the rigors of 140-character communication, even legitimate tweets tend to read like Nigerian phishing scams, while the hucksters often hide their pitches in links. So Twitter simply puts the onus on users to report offending accounts.

 

But a fascinating recent study from Imperial College London suggests a new approach. Borrowing some tricks from computational neuroscience, coauthors Gabriela Tavares and Aldo Faisal have come up with an algorithm that can tell—with 85 percent accuracy—whether a Twitter account is home to a bot or (worse) a corporate shill instead of a regular person.

 

It’s all in the timing. By analyzing the timestamps on 165,000 tweets, the researchers found that these three user types—individuals, companies, and robots—have very distinct activity patterns. Think of it as temporal fingerprinting. The approach could eventually be used to create more effective filters for all kinds of social networks.

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Tweet Archivist Desktop | #dataviz #socialmedia

Tweet Archivist Desktop | #dataviz #socialmedia | e-Xploration | Scoop.it
Tweet Archivist, an desktop application tool to archive, analyze, visualize, save and export tweets.
luiy's insight:
Tweet Archivist Desktop is a Windows application that helps you archive tweets for later data-mining and analysis. Start a search with Tweet Archivist and it will get as many results as it can. Then, leave Tweet Archivist running and it will poll Twitter for that search as frequently as once every five minutes.
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The role of Twitter in the life cycle of a scientific publication

Twitter is a micro-blogging social media platform for short messages that can have a long-term impact on how scientists create and publish ideas. We investigate the usefulness of twitter in the development and distribution of scientific knowledge.

Via Pierre Levy
luiy's insight:

Many scientists are making the move towards social media in order to accelerate  and amplify their scientific impact (Fausto et al. 2012; Fox 2012; Piwowar 2013). One in 40 scientists is active on Twitter (Priem et al. 2012a), 25,000 blog entries have been indexed on the Research Blogging platform, and 2 million scientists are using Mendeley, a reference sharing tool (Piwowar 2013). Here, we consider 140 how social media, and Twitter in particular, can influence the life cycle of scientific publication, from inception and collaboration on a spark of an idea to the communication of a finished product. Specifically, we evaluate and discuss the benefits of Twitter for (1) increasing scholarly connections and networks, (2) quickly developing ideas through novel collaborations and pre-review, and (3) amplifying the dissemination and discussion of scientific knowledge both within and beyond the ivory tower of academia.

 

 

The impact of scientific papers has traditionally been measured in terms of
numbers of citations (Neylon and Wu 2009). Tweeting can influence this impact metric. For example, articles published in the Journal of Medical Internet Research that were tweeted about frequently in the first three days following publication were 11 times more likely to be highly cited 17 to 29 months later than  less tweeted articles (Eysenbach 2011). In fact, top-cited articles could be predicted quite accurately from their early tweeting frequency (Eysenbach 2011). In a separate study of ~4600 scientific articles published in the preprint database  arXiv.org, Shuai et al. (2012) found that papers with more mentions on Twitter were also associated with more downloads and early citations of papers, although the causality of these relationships is unclear (Shuai et al. 2012).

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Antonio Figueiredo's curator insight, May 19, 2013 4:54 AM

Paper available on PeerJ discusses the role of Twitter in the lifecycle of a scientific publication.

Renato P. dos Santos's curator insight, May 20, 2013 10:07 AM

estudo conclui que o Twitter contribui para a publicação científica no século 21

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How To Detect #Communities Using Social Network Analysis | #SNA

How To Detect #Communities Using Social Network Analysis | #SNA | e-Xploration | Scoop.it
luiy's insight:

Think of communities as very similar to the segments identified in a brand’s customer segmentation model. (With demographics analysis layered on, you might even find that they’re the same.)

While direct marketing communications is often customized by segment, historically this hasn’t been something brands have done in social. But, using social network analysis and also Twitter & Facebook ad targeting, it’s possible to send specific messages to specific groups of people.

 

Powered by Pulsar TRAC these could be people engaging in a specific conversation, individuals sharing a piece of content online, or the followers of an account on Twitter. Any group of people, in essence, as long as we can define that audience through some property of its behaviour in social media – such as keyword, user bio, or location.

 

Community analysis allows brands to really understand the behavior of their audiences in a way they can’t replicate with offline, non-social data.

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Where was Ferguson in my Facebook feed? | #algorithms #filtering

Where was Ferguson in my Facebook feed? | #algorithms #filtering | e-Xploration | Scoop.it
There were big differences in the content related to Ferguson on Twitter and Facebook. Was the reason what users wanted from each, or the sites' algorithms?
luiy's insight:

A number of journalists and commentators observed a jarring disconnect between the mostly uncontroversial posts on Facebook (like chatter about celebrities taking the Ice Bucket Challenge to raise funds for the fight against Lou Gehrig’s disease), and the stream of visceral reportage from the tense scene in Ferguson, where citizens had gathered to protest the August 9th police killing of an unarmed black teen, Michael Brown.

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Mapping the global #Twitter heartbeat: The geography of Twitter | #datascience

Mapping the global #Twitter heartbeat: The geography of Twitter | #datascience | e-Xploration | Scoop.it
Mapping the global Twitter heartbeat: The geography of Twitter
luiy's insight:

In just under seven years, Twitter has grown to count nearly three percent of the entire global population among its active users who have sent more than 170 billion 140–character messages. Today the service plays such a significant role in American culture that the Library of Congress has assembled a permanent archive of the site back to its first tweet, updated daily. With its open API, Twitter has become one of the most popular data sources for social research, yet the majority of the literature has focused on it as a text or network graph source, with only limited efforts to date focusing exclusively on the geography of Twitter, assessing the various sources of geographic information on the service and their accuracy. More than three percent of all tweets are found to have native location information available, while a naive geocoder based on a simple major cities gazetteer and relying on the user–provided Location and Profile fields is able to geolocate more than a third of all tweets with high accuracy when measured against the GPS–based baseline. Geographic proximity is found to play a minimal role both in who users communicate with and what they communicate about, providing evidence that social media is shifting the communicative landscape

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#Naoyun – Visualize Live Twitter Activity | #SNA #gephi

#Naoyun – Visualize Live Twitter Activity | #SNA #gephi | e-Xploration | Scoop.it
luiy's insight:

The TwitterStreamer is the main class that manage the Twitter API . This class also load a class that extends TwitterGephiStreamer. Each time the Twitter Api get a new Status, the TwitterStreamer call the newStatus method from the TwitterGephiStreamer class.

On this method, there is the « Network Logic ». I called « Network Logic » all the processes and the rules to create a network from Twitter status.

 

For the moment, Naoyun have 3 network logic :

 

- TwittFullGrapher : Makes a complete graph by representing users, hashtags, tweet, media, links and their connection. The « Smart » version implemented in Naoyun won’t represent tracked hashtag to improve the visibility of the graph.


- TwitterUserNetwork : Represent only the relation between users.


- GeoTwitt : Just display Twitt with Geo localisation

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The one million tweet map #onemilliontweetmap

The one million tweet map #onemilliontweetmap | e-Xploration | Scoop.it
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Francisco Restivo's curator insight, August 7, 2014 7:09 AM

What's happening, in real time. You'll be surprised with all you can discover.

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Sentiment analysis about #Pemex in #Mexico | #SNA #dataviz #politics

Sentiment analysis about #Pemex in #Mexico | #SNA #dataviz #politics | e-Xploration | Scoop.it
luiy's insight:

Los gráficos que presentamos representan los actores principales que participarón en Twitter en el contexto del fenómeno de las reformas energéticas en México. Se analizó @Pemex, como un actor principal en el cambio político y económico en México. Por lo cual, se observa en los gráficos el usuario @Pemex y sus relaciones con otros actores en una red de tipo “Ego Network”. Asimismo, se clasificarón los comentarios según el sentimiento del mensaje, los cuales son representados en el color y el grueso de cada vértice.

 

El tamaño y color de nodos y vertices representan atributos y valores cuantitativos y cualitativos de figuras políticas, medios de comunicación, ciudadanos y otros actores emergentes. De esta manera se pueden observar los actores mas influyentes en la red de @Pemex.

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Which social media network type is your topic? Which did you want it to be? | #SNA #datascience

Which social media network type is your topic?  Which did you want it to be? | #SNA #datascience | e-Xploration | Scoop.it
There are at least six different types of social media network structures present in systems like Twitter and other services in which people are able to reply to one another. Each of the six patter...
luiy's insight:

This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.

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Twitter’s Root Injustice | #socialmedia #cyberculture

Twitter’s Root Injustice | #socialmedia #cyberculture | e-Xploration | Scoop.it
For the past three years this post has been stuck in the drafts folder of my computer.
luiy's insight:
Early adopters rule Twitter. If Twitter wants to survive it will need to make the fight for power that rages inside the platform more fair for all.

 

How To Make Twitter More Fair and Competitive

The story of my experience on Twitter is the story that is celebrated—but rarely the case for most people. When I joined Twitter in 2009 it was like showing up to a half-settled frontier town. I could talk with people who would normally never talk to me, I could gain a following in fields where I was years younger than most, and I could do this all from a laptop in my underwear.

Twitter was magical in 2009 because the platform was getting a daily flood of users of all stripes eager to find accounts to follow. We’ll never be able to get back to that era of Twitter, but there are a ton of things Twitter can do to help users....

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Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet Rate in Twitter Network | #datascience

Presented at SocialCom 2010 conference Aug 21st 2010
luiy's insight:

Retweeting is the key mechanism for information
diffusion in Twitter. It emerged as a simple yet powerful way of
disseminating information in the Twitter social network. Even
though a lot of information is shared in Twitter, little is known
yet about how and why certain information spreads more widely
than others. In this paper, we examine a number of features that
might affect retweetability of tweets. We gathered content and
contextual features from 74M tweets and used this data set to
identify factors that are significantly associated with retweet rate.
We also built a predictive retweet model. We found that, amongst
content features, URLs and hashtags have strong relationships
with retweetability. Amongst contextual features, the number of
followers and followees as well as the age of the account seem to
affect retweetability, while, interestingly, the number of past
tweets does not predict retweetability of a user’s tweet. We
believe that this research would inform the design of sensemaking 

and analytics tools for social media streams.

 

Article in :  http://www-users.cs.umn.edu/~echi/papers/2010-socialcom/2010-06-25-retweetability-cameraready-v3.pdf

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Der ZEIT ONLINE #Twitter-Monitor - #dataviz

Der ZEIT ONLINE #Twitter-Monitor - #dataviz | e-Xploration | Scoop.it
Welche Wahlkampfthemen bewegen auf Twitter? Worüber twittern Politiker, Journalisten, Lobbyisten, Wähler? Unser fortlaufend aktualisierter Twitter-Monitor zeigt es.
luiy's insight:

Twitter monitor.

 

Welche Wahlkampfthemen bewegen die Twitter-Nutzer? Diese Grafik zeigt es. Wir werten dafür die wichtigsten 10.000 deutschsprachigen Twitter-Accounts aus. Berücksichtigt werden dabei Followerzahl, Erwähnungen und Aktivität. Die Grafik dokumentiert, welche Entwicklung bestimmte Themen in den vergangenen 48 Stunden und seit Anfang August genommen haben. Und, wer die Tweets jeweils absetzt: Politiker, Journalisten, Verbände, Wähler. Zu jedem Thema blenden wir zudem die Tweets mit den häufigsten Retweets ein. Weitere Hintergründe

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Twitter Archiving Google Spreadsheet TAGS v5 MASHe | #dataviz #extracting #SNA_indatcom

Twitter Archiving Google Spreadsheet TAGS v5 MASHe | #dataviz #extracting #SNA_indatcom | e-Xploration | Scoop.it
For a couple of years I've been sharing a Google Sheet template for archiving searches from Twitter. In September 2012 Twitter announced the release of a new version of their API (the spreadsheet uses this to request data from Twitter).
luiy's insight:
Twitter Archiving Google Spreadsheet TAGS v5

For a couple of years now to support my research in Twitter community analysis/visualisation I’ve been developing my Twitter Archiving Google Spreadsheet (TAGS). To allow other to explore the possibilities of data generated by Twitter I’ve released copies of this template to the community.

 

In September 2012 Twitter announced the release of a new version of their API (the spreadsheet uses this to request data from Twitter). Around the same time Twitter also announced that the old version of their API would be switched off in March 2013. This has required some modification of TAGS to work with the new API. The biggest change for TAGS is that all requests now need authenticated access.

So here it is:

 

*** Twitter Archive Google Spreadsheet – TAGS v5.0 ***


[If the first link doesn't work try Opening this Spreadsheet and File > Make a copy]

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#Algorithm Writes People's Life Histories Using Twitter | #dataviz

#Algorithm Writes People's Life Histories Using Twitter | #dataviz | e-Xploration | Scoop.it

“If you tweet about your life, a new algorithm can identify your most significant events and assemble them into an accurate life history, say the computer scientists who built it (Algorithm Writes People's Life Histories Using Twitter”


Via Claudia Mihai
luiy's insight:

Twitter allows anyone to describe their life in unprecedented detail. Many accounts provide an ongoing commentary of an individual’s interests, activities and opinions. 


So it’s not hard to imagine that it’s possible to reconstruct a person’s life history by analysing their Twitter stream.

But doing this automatically is trickier than it sounds. That’s because most Twitter streams contain news of important events mixed up with entirely trivial details about events of little or no significance. The difficulty is in telling these apart.


Ref:arxiv.org/abs/1309.7313 : Timeline Generation: Tracking individuals on Twitter

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