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Here's How Many Messages Men Have To Send To Women On A Dating Site To Be Sure Of Getting A Response

WALTER HICKEY

Business Insider

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. 

Cómo Node XL ayuda a investigar las comunidades de Twitter

Look Out Klout, These Twitter Influencer Maps Are Amazing

Mark Fidelman, Contributor
Forbes

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.

Borgatti-Halgin: Analizando redes de afiliación

Borgatti_Analyzing Affiliation Networks

Publish at Calameo or read more publications.

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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

Social Media Today

Smith: Creando mapas y mediciones con Node XL

2013 NodeXL Social Media Network Analysis from Marc Smith

Factor de impacto en los journales: ¿Para que sirven?

Journal impact factors: what are they good for?

The ISI journal impact factors for 2012 were released last month. Apparently 66 journals were banned from the list for trying to manipulate (through self-citations and “citation stacking”) their impact factors.

There’s a heated debate going on about impact factors: their meaning, use and mis-use, etc.  Science has an editorial discussing impact factor distortions.  One academic association, the American Society for Cell Biology, has put together a declaration (with 8500+ signers so far)–San Francisco Declaration on Research Assessment (DORA)–highlighting the problems caused by the abuse of journal impact factors and related measures. Problems with impact factors have in turn led to alternative metrics, for example see altmetrics.

I don’t really have problems with impact factors, per se.  They are one, among many, measures that might be used to measure journal quality.  Yes, I think some journals indeed  are better than others.  But using impact factors to somehow assess individual researchers can quickly lead to problems.  And, it is important to recognize that impact factors assume that articles within the journal are homogeneous, though within-journal citations of course are radically skewed.  Thus a few highly-cited pieces essentially prop up the vast majority of articles in any given journal. Citations might be a better measure, though also highly imperfect.  If you want to assess research quality: read the article itself.

On the whole, article effects trump journal effects (as Joel Baum’s article also points out, see here).  After all, we all have one-two+ favorite articles, published in some obscure journal no one has ever heard of.  Just do interesting work and send it to journals that you read.  OK, that’s a bit glib.  I know that all kinds of big issues hang in the balance when trying to assess and categorize research: tenure and promotion, resource flows, etc. Assessment and categorization is inevitable.

A focus on impact factors and related metrics can quickly lead to tiresome discussions about which journal is best, is that one better than this, what are the “A” journals, etc.  Boring.  I presented at a few universities in the UK a few years ago (the UK had just gone through its Research Assessment Exercise), and it seemed that many of my interactions with young scholars devolved into discussing which journal is an “A” versus “A-” versus “B.”  Our lunch conversations weren’t about ideas – it was disappointing, though also quite understandable since young scholars of course want to succeed in their careers.

Hopefully enlightened departments and schools will avoid the above traps and focus on the research itself.  I think the problems of impact factors are well-known by now and hopefully these types of metrics are used sparingly in any form of evaluation, and only as one imprecise datapoint among many others.

[Thanks for Joel Baum (U of Toronto) for sending me some of the above links.]

Orgtheory.net

El Facebook del Senado estadounidense

The Senate as Facebook

By Chris Cillizza

Ever wonder what the Senate would look like viewed through the lens of Facebook?  Us too.

This is Facebook.

Now, thanks to Yahoo’s Chris Wilson, we know. Using Senate votes, Wilson has created a mini-social network of the world’s greatest deliberative body.

Here’s how he explains it:

“For every member, I calculated which other senators voted the same way at least 75 percent of the time. In effect, this organizes the Senate as a mini-Facebook of 100 users, in which any given pair of senators are friends if they meet this 75-percent threshold….Visualizations like this one work by treating the senators as particles that repel one another, and treating the connections between them as springs that hold them together. Because the Democrats vote so cohesively, with few defectors, they are held together by a large number of springs.”

In the chart below, you can see the Senate as a whole or sort via specific Senator to see whether they have any ties — meaning they vote with a colleague 75 percent or more of the time — to other Senators.

What’s clear from the chart is that while Senate Democrats are more closely aligned than Republicans in their voting patterns so far in 2013 — Wilson notes that 22 Democrats have voted exactly the same on every vote this year — there are very few ties between the two parties. The two members sitting in the middle are Republicans Sens. Lisa Murkowski (Alaska) and Susan Collins (Me.), two of the noted moderates in the chamber.

Then there is the strange case of Louisiana Sen. David Vitter (R) and New Jersey Sen. Frank Lautenberg (D). Neither man has voted with any other senator more than 75 percent of the time during 2013. (Lautenberg, who is retiring in 2014, hasn’t even voted with any other Senator 65 percent of the time.)

The most obvious storyline from the amazing tool Wilson has built is that the two parties in the Senate have, at least by their voting records in 2013, almost nothing in common. That affirms the widening partisan divide that we’ve observed in the Senate and in politics more broadly over the past few years.

Fiddle around with Wilson’s infographic. It’s a great tool that can spawn a thousand insights into how the Senate works (and doesn’t). What’s yours?

The Fix