lunes, 22 de julio de 2013
domingo, 21 de julio de 2013
sábado, 20 de julio de 2013
Los jovénes ricos más proclives a conductas de levante
Why Wealthy College Kids Are More Into 'Hooking Up' Than Their Poorer Classmates
The New York Times' recent article on the hookup culture at the University of Pennsylvania has garnered many fiery responses, with current and former Penn students blasting the piece for everything from its casual treatment of rape to its reliance on anonymity.
Even the Daily Pennsylvania — UPenn's student newspaper — has come out swinging against the Times, noting:
The response has been largely critical, and for good reason: In her failed attempt to glimpse a part of Penn’s culture, Taylor drew conclusions that inaccurately represented and overly generalized the University’s student body.
Despite its many issues, one tidbit deeply buried in the article deserves more attention.
According to the article, two researchers who followed a group of women at IndianaUniversity from their freshman to senior years found that "women from wealthier backgrounds were much more likely to hook up, more interested in postponing adult responsibilities and warier of serious romantic commitment than their less-affluent classmates. The women from less-privileged backgrounds looked at their classmates who got drunk and hooked up as immature."
In an excellent breakdown of the Times' article on Jezebel, writer Tracy Moore sums this up as "Rich ladies like to PARTY, poor girls have to keep it real...virginal." Moore notes that she finds class-based attitudes towards casual sex "actually really fascinating," although "it's almost never explored."
While the research on privilege and hooking up is sparse at best, a 2009 study from Laura Hamilton and Elizabeth A. Armstrong — the two researchers quoted in the Times — sheds some light on this often overlooked field. After speaking with a diverse group of women at a Midwest university, here's what they found:
- The dominant college culture "reflects the beliefs of the more privileged classes."
- Less privileged women's disinterest in hooking up could be "characterized by a faster transition into adulthood."
- For less privileged women not interested in hooking up, "college life could be experienced as mystifying, uncomfortable, and alienating."
- 40% of less privileged women left the university, compared to 5% of more privileged women, and "In all cases, mismatch between the sexual culture of women’s hometowns and that of college was a factor in the decision to leave."
- Many less privileged women left the university because of pressure from boyfriends or husbands back home, or to end hometown speculation that they were now a "slut."
- Because less privileged women "had less exposure to the notion that the college years should be set aside solely for educational and career development"they often did not view "serious relationships as incompatible with college life."
While the entire report is fascinating and worth a read, its findings are not without opponents. Although not a direct response to Hamilton and Armstrong's paper, a 2012 study on college sexual activity from researchers at Georgia Southern University found that social class is not a "significant predictor of hooking up." Clearly, this is an area that still requires some study.
viernes, 19 de julio de 2013
Los hombres gastan más mensajes en hacer contacto
Here's How Many Messages Men Have To Send To Women On A Dating Site To Be Sure Of Getting A Response
Yesterday, we posted a chart that Josh Fischer at Snap Interactive (STVI: OTC BB) sent us based on analytics from their dating website Are You Interested.
It shows the likelihood that a someone on AYI.com responds to a message from a member of the opposite sex given their age difference. To the left, -10 means the sender was 10 years younger, on the right, the sender was 10 years older, with zero indicating that the sender and the recipient are the same age.
Here are the two plots for men responding to messages from women (blue) and women responding to messages from men (red).
Josh Fischer / SNAP Interactive
So, we can see that women are much more selective than men when it comes to responding to messages. Not exactly Nobel-quality findings here, but it's definitely interesting to see the exact levels of response.
But for folks in the dating game, how is this information actually usable?
Well, let's find out how many messages the average man will have to send to a woman his own age in order to guarantee various levels of response, and vice versa.
We can't guarantee a response, per se, but we can say how confident we are that these average bachelors and bachelorettes will receive at least one response given the number of messages they send.
We know, from the chart above, that a woman who sends a message to a man her own age has a 17.5% likelihood of receiving a response to that message.
We know that a man who sends a message to a woman his own age has a 4% likelihood of receiving a response to that message.
Extrapolating from there, here's how confident men and women can be that they will receive a response given the number of messages they send en masse:
Walter Hickey / BI
Fascinating.
An average man who sends 18 messages to women his own age can be 50% certain he'll receive at least one response. For women, they need to send only 5 messages to be 50% certain they'll get a response.
Looking at higher confidence levels, if a woman wants to be 90% certain she'll receive a response from a man her own age, she'll have to send 13 messages. A man will have to send 58 messages.
Finally, to be 99% certain she'll receive a response, a woman must send 25 messages to men her own age.
A man will have to send 114 .
This leads us to believe that one potential cause of the disparity between the male response rate and female response rate is the system itself.
If men must spam women with messages in order to elicit a response, then women will be more selective when responding to the surplus in general. Since women are understandably disinclined to respond to all the messages, men must send out more in order to guarantee any response. It's cyclic.
Everyone is acting in their own self interest, inadvertently leading to further imbalances in the system.
It's one of the fundamental issues with online dating in general.
Anyway, best of luck out there folks.
jueves, 18 de julio de 2013
Cómo Node XL ayuda a investigar las comunidades de Twitter
Look Out Klout, These Twitter Influencer Maps Are Amazing
Mark Fidelman, ContributorForbes
What if instead of a score, you could visualize the impact a person, business or topic has in a social network? What if instead of using complicated listening tools, you could see in an instant who is talking about your company or its products and how you’re connected to them? What if you could tell who the major influencers or connectors are that everyone else is listening to? From what I’ve seen from the social network maps from NodeXL, this is all possible and a whole lot more.
It’s difficult to have a discussion today with any organization who doesn’t tell you some version of this: Advertising is becoming less effective, yet I can’t figure out why, let alone understand how to adapt. I keep hearing that advocate and influencer marketing is more effective, but I haven’t figured out how to find the right people that clearly impact my potential buyer.
Well allow me to speculate. We now live in an increasingly saturated world — a world where ads are placed nearly everywhere – a world with so much marketing noise it’s hard to separate fact from fiction – a world where even the big TV networks and news corporations no longer have the ability to create overnight product successes.
Our saturated world is causing people to tune out of traditional forms of advertising and turn to industry experts or influencers to make sense of it for them. The old magic formula for selling product through proven media channels has disappeared. So smart organizations are turning to thought leaders, influencers and experts to endorse and promote their products.
But how do you accurately find them and how do you know who is really having an impact on the organization? Moreover, can I look at the influencer or expert’s network to understand who they influence?
Take a look at my NodeXL Twitter Social Graph given to me by Social Network Theory expert Marc Smith:
To the untrained eye, the map is a little difficult to decipher (Smith is working on simplifying it for the rest of us) but I am at the center of that blue mass in section G1. Smith tells me that the Forbes community in G3 is supportive of my articles (that’s good to know) but there are a whole lot of people in G2 that I don’t know and are not connected with that are discussing topics related to me. Smith says that I should make the effort to get to know them because they could be very helpful with my goals. In G4 – G34, those are communities of people that I may or may not know – but are discussing my content – and I should seek to extend my network into those communities.
The Six Basic Types of Twitter Social Networks
Below using analysis from NodeXL, Smith outlines the 6 major types of Twitter social network types. What’s exciting is that this tool empowers companies to understand not only the social network around them, but who is most important in it. This huge expansion in social network understanding allows organizations to find hidden prospective customers, connect with the true influencers in their industry, and A/B test the impact of their social campaigns (NodeXL allows multiple ways of looking at the network data).
Let’s briefly explore each type, and then I’ll provide some relevant, real world examples for you to look at on the NodeXL site:
1. Polarized Network: Most often seen in politics or political issues, this pattern emerges when two groups are split in their opinion on an issue. Here’s one on Egyptian crisis.
2. In-Group Network: Seen at conferences and tight knit groups of people, this type of network rarely ventures outside of its membership. A big miss in most cases if you are a brand. Here’s an example of one for Social Business.
3. Brand/Public Topic Network: Most often seen when a person or company becomes a brand and people other than your customers are talking about you. Notice all of the disconnected users that are isolated from the brand and not connected. Car companies are a good example of a brand network.
4. Bazaar Network: Most often seen with medium sized companies or political issues with various community involvement (see the Texas Abortion Law example)
5. Broadcast Network: As its name implies, these individuals or companies have the power to light up the network – or in this baseball team example, have the ability to light up the Twitter scoreboard. News organizations also display a classic broadcast pattern.
6. Support Network: Think customer support. These types of networks are known to be good at customer service.
In case you’re wondering, a lot of this research came out of Microsoft or in partnership with Microsoft research. The following are other people and institutions that are involved including Stanford, The University of Washington and the University of Maryland.
Truly, these maps are very impressive and a big leap for organizations wishing to understand their impact in social networks. But for companies and individuals to take advantage of the opportunities presented in the network maps, NodeXL will need to make it easier for users to understand them and take action. Smith assures me this is right around the corner.
In fact, Smith tells me the maps will be much more interactive and actionable. That will make finding influencers and new customers incredibly easy.
That’s a big game changer.
martes, 16 de julio de 2013
lunes, 15 de julio de 2013
8 herramientos analíticas de Twitter
8 Excellent Twitter Analytics Tools to Extract Insights from Twitter Streams
Yung Hui Lim
Twitter is now the third most popular social network, behind Facebook and MySpace (Compete, 2009). A year ago, it has over a million users and 200,000 active monthly users sending over 3 million updates per day (TechCrunch, 2008). Those figures have almost certainly increased since then. With the torrential streams of Twitter updates (or tweets), there's an emerging demand to sieve signals from noises and harvest useful information.
Enter Twitter Analytics, Twitter Analysis, or simply just Analytwits (in the tradition of Twitter slang). These analytics tools are growing in numbers; even Twitter is developing them.
Besides Twitter Search, the following 8 Analytwits are some of the more useful web applications to analyze Twitter streams. Each of these tools serve specific purpose. They crawl and sift through Twitter streams; also, aggregate, rank and slice-and-dice data to deliver some insights on Twitter activities and trends. There's no single best analytic tool available but use in combination, they can extract interesting insights from Twitter streams.
8 Great Tools for Social (Twit)telligence
TWITALYZER provides activities analysis of any Twitter user, based on social media success yardsticks. Its metrics include (a) Influence score, which is basically your popularity score on Twitter (b) signal-to-noise ratio (c) one's propensity to ‘retweet' or pass along others' tweets (d) velocity - the rate one's updates on Twitter and (e) clout - based on how many times one is cited in tweets. Its Time-based Analysis of Twitter Usage produces graphical representation of progression on various measures. Using Twitalyzer is a easy; just enter your Twitter ID and that's it! It doesn't require any password to use its service. Speed of analysis is depending on the size of your Followed and Followers lists.
MICROPLAZA offers an interesting way to make sense of your Twitter streams. Called itself “your personal micro-news agency,” it aggregates and organizes links shared by those you follow on Twitter and display them as newstream. Status updates that contain similar web links are aggregated into 'tiles.' Within a tile, you can see updates from those you follow and also those you don't. Another interesting feature is ‘Being Someone', which you can peek into someone's world and see their 'tiles'; designed to facilitate information discovery. You can also organize those you follow into groups or ‘tribes'. You can create, for example, a knitting ‘tribe' to easily what URLs your knitting friends are tweeting. In addition, you can bookmark 'tiles' for future reference. Its yet-to-be-released feature, Mosaic, allows users to group together the bookmarked 'tiles' and turn them into social objects - for sharing and discussion. At the time of this posting, MicroPlaza is still in private beta.
TWIST offers trends of keywords or product name, based what Twitter users are tweeting about. You can see frequency of a keyword or product name being mentioned over a period a week or a month and display them on a graph. Select an area on the graph to zoom into trend for specific time range. Click on any point on the graph to see all tweets posted during a specific time. One can also see the latest tweets on the topic. Twist also allows you do a trend comparison of two (or more) keywords. Its graphs are embeddable on any website. A simple but effective tool for trending, similar to what Google Trends is doing for search queries.
TWITTURLY tracks popular URLs tracker on Twitter. With Digg-style interface, it displays 100 most popular URLs shared on Twitter over the last 24 hours. On Digg, people vote for a particular web content, whereas on Twitterurly, each time a user share a link, it is counted as 1 vote. This is a good tool to see what people are ‘talking' about in Twitterville and see total tweets that carry the links. Its URL stats provides information on number of tweets in last 24 hrs, last 1 week and last 1 month. It also calculates total estimated reach of the tweets. Another interesting site is Tweetmeme, which can filter popular URLs into blogs, images, videos and audios.
TWEETSTATS is useful to reveal tweeting behavior of any Twitter users. It consolidates and collates Twitter activity data and present them in colorful graphs. Its Tweet Timeline is probably the most interesting, as it shows month-by-month total tweets since your joined Twitter (TweetStats showed Evan Williams, co-founder of Twitter, started tweeting since March 2006; 80 tweets during that month). Twitterholic can also show when a person joined Twitter but not in graphical format. Other metrics include (a) Aggregate Daily Tweets - total tweets, by day (c) Aggregate Hourly Tweets - total tweets, by hour (d) Tweet Density: hourly Twitter activities over 7 days period (e) Replies to: top 10 persons you've replied and (f) Interfaces Used: top 10 clients used to access Twitter. In addition, its Tweet Cloud allows you to see the popular words you used in your tweets.
TWITTERFRIENDS focuses on conversation and information aspects of Twitter users' behaviors. Two key metrics are Conversational Quotient (CQ) and Links Quotient (LQ). CQ measures how many tweets were replied whereas LQ measures how many tweets contained links. Its TwitGraph displays six metrics - Twitter rank, CQ, LQ, Retweet Quotient, Follow cost, Fans and @replies. Its interactive graph (using Google Visualization API) can displays relationships between two variables. In addition, you can search for conversations between two Twitter users. This app seems to slice-and-dice data in more ways compared to other applications listed here.
THUMMIT QUICKRATE offers sentiments analysis, based on conversations on Twitter. This web application identifies latest buzzwords, actors, movies, brands, products, etc. (called ‘topics') and combines them with conversations from Twitter. It does sentiment analysis to determine whether each Twitter update is Thumms up(positive), neutral or Thumms down (negative). Click on any topic to display opinions on the topic found on Twitter. In addition, it allows people to vote on topics via its website or mobile phones. The idea behind this app is good but still has some kinks to work out.
TWEETEFFECT matches your tweets timeline with your gain/lose followers timeline to determine which tweet makes you lost or gain followers. It analyze the latest 200 tweets and highlights tweets that coincides with you losing or gaining two (or more) followers in less than 5 minutes. This application simplistically assumed that your tweet is the sole factor affecting your gain/lose followers pattern. But, in reality, there are many other factors involved. Nevertheless, TweetEffect is still a fun tool to use; just don't take the results too seriously.
Let's Continue the Discourse on Twitter
Which of the abovementioned Twitter analytics you like the most? How can these tools generate revenue? Have you discovered any other interesting Twitter analytics? Share your thoughts on Twitter; find me @limyh
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