I’ve been following news and opinion pieces concerning the economics of austerity. It so happened that yesterday was May Day and some large demonstrations concerning austerity took place.

Today I logged into Hootsuite and on the Nexalogy Search app, I searched for #austerity.

I was a bit surprised by today’s result.

#greece, #europe — no surprise there. #mayday was not surprising either.

But I didn’t expect to see #hiv. It’s number 6 in the top ten hashtags.

Drilling down into the tweets, we can see people responding to and retweeting the Al-Jazeera article  The true cost of austerity. In fact , out of the 5 top links displayed in the Nexalogy Search App, 4 of them were different links pointing to this article.

Here’s a quote :

The joint British and US-led research says more than 10,000 suicides have been recorded during what they call ‘The Great Recession’.

And as many as a million cases of depression have been diagnosed – that is a rise of 10 percent. It says five million Americans have lost access to healthcare while 10,000 families in Britain have lost their homes.

HIV rates in Greece have risen by more than 200 percent since 2011, because of cuts in funding. And the research suggests rising unemployment is pushing more young people to take drugs.

Not surprisingly, a Google News search on the Internet for ‘austerity’ brings up this same article, as well as others related to #mayday protests in Europe, particularly in Athens, Brussels and London.

With the Nexalogy Search, I was able to see not only what people were reading about yesterday and this morning, but I also gained insight into what’s in these articles, and what’s moving people to tweet about it.

I find a rise in HIV of 200% alarming, and so do a lot of other people.

let’s see what happens if we search now for #hiv

Top Hashtags for #hiv

Interestingly when I search for “hiv” in google news i see no connection to austerity. However, in the twittersphere conversations, there is a connection.

Looking in more detail in the interest map for the ‘#hiv’ search, we can see  a separate but important conversation happening, highlighted in orange below:

Interest Map for '#hiv'

Sure enough, this is a “re-tweet” cluster. We can often see in interest maps  a separate cluster that binds together certain words but seems somewhat apart from the main themes. Often a cluster is caused by a large number of re-tweets of a single tweet.

In this case they are re-tweets of the same article by Al-Jazeera that we described from #austerity search.

What’s nice about Nexalogy Search is the organic connections that link topics together.  In some cases, such as the Google News search for ‘hiv’, we see that an analysis of the  Twittershere is finding connections between topics and articles that a straight-up news search didn’t find.

This is because searching for news on Twitter is less about finding top news than finding the news that people are talking about, and it’s the people talking (tweeting) , thinking, processing, and reacting to the news that I find interesting.

It’s in this processing of the news by readers and tweeters that the connections surface.

On Tuesday April 23, 2013  at 1:07pm the Associated Press Twitter account was hacked and the following tweet was posted :

It was confirmed a minute later that in fact nothing out of the ordinary had occurred at the white house, and that president Barack Obama was fine. Yet,  in that one minute, the Dow Jones industrial average dropped 1% before recovering. You can see this dip below very clearly around 1pm in the graph below, still visible the next day.

Many folks out there have no connection to twitter, and can barely see its usefulness. People said the same thing about blogs in the early 2000s. Some of us may not like twitter, or do not use it on a regular basis. It’s easy if you are not inclined to use these technologies to be puzzled as to why anybody else would. It’s easy to fail to see the relevance of twitter if it’s not something that you use.

But is there any doubt now that, at least for certain tweeters, that people are not only listening but prepared to act as a result of a single tweet ?

Of course there are a large number of  people tweeting about how bored they are, posting pictures of their cats and their baby’s antics, but there are other accounts whose tweets affect people all over the world.

For an account like Associated Press’ twitter account, people will act on the tiniest tweet, to get that edge over the others, to react more quickly than others, for the sake, for example of catching a market crash before others do.

But even though in this example the damage lasted only a minute, there is definitely the question of whether seeing tweets on your Twitter wall is enough. It’s important to get an overall view of what is happening in the world, not just from a single tweet, but from a larger community. AP getting hacked is just another example of how a particular feed, which under most circumstances provides reputable information, can be hijacked for potentially disastrous results.

If we look the situation in one of Nexalogy’s free tools, the Nexalogy Search app on Hootsuite, searching for the hashtag ‘#ap’, this is what we see :

The immediate constellation of words surrounding ‘#AP’ tells us a different story. At a glance, we see that AP account on twitter has been hacked, we  can also see other relevant conversations surrounding this constellation of terms, conversations concerning vulnerabilities of social media accounts.

It’s important to be able to not only follow important tweeters, but also to be able to place the concepts and links that are posted into context and get a sense of what is happening in the ongoing twitter conversations within the “twittersphere”.

Our nexalogy tools are designed especially to do just that.

So the next time something happens that freaks you out to the point of selling everything you own, go first to Hootsuite sign-up and try our free Nexalogy Search App to get a sense of the larger conversation.

Today Nexalogy’s Hootsuite integrated applications “Nexalogy Search” and “NexaMe” were featured in the La Presse as application of the week in the Technology section. We’re super happy to be featured, so to read the article click here.

As the article correctly points out, more and more companies are becoming tuned in to social media and its influence on their marketing, sales and public relations activities. Nexalogy technology on Hootsuite helps both individuals and enterprises monitor, track, and understand key conversations on social media.

 

5 things you can do with Nexalogy Tools:

1. Track and monitor hashtags and issues, so you never miss an important message or update that relates to your brand, interest-area, or sector.

2. Understand conversations, through lexical-mapping so that no matter how big the conversation is, you will be tuned-in to the main themes, and aware of emerging discussion topics that are trending.

3. Identify, add and engage with actors. Nexalogy technology will allow you to see who the key actors are in relevant discussions. Nexalogy helps you to add people that you ‘need-to-know’ and better understand who the influencers are and what they are saying.

4. Drill down into social media conversations, going to source texts to identify which messages have the most potential, mine content, and data-sculpt.

5. Generate reports and insights based on factual measurement of the social media landscape over time.

 

To try out the Nexalogy Search and NexaMe go here to Hootsuite

 

Past examples of Nexalogy Tech in action include the following examples, and be sure to consult our library for more information:

Nexalogy tech helped The United Church of Canada to launch and monitor a Social Media based youth engagement program for their church that included key events and messages. The Nexalogy tech was used to identify who was discussing what, when, and where, and contributed towards the youth uptake of their programs by engaging social media discussions and popular content.

Nexalogy tech helped partner firms obtain a Northwest Territories Tourism contract by monitoring discussions about two different television shows and the NWT tourism Ad campaign simultaneously. The Nexalogy tech showed all discussions about Arctic Air, and Ice Pilots, two NWT focused television shows, and the NWT Ad campaign “SpectacularNWT.” The results led to a refined bid and advanced intel on main themes of discussion around the shows and highlighted the success of specific episodes and promotions.

A Nexalogy tech microsite on Economic Development helped ROI vision reach out to and engage Economic Development boards across North America in order to facilitate their participation in an online webinar. The webinar featured Nexalogy tech results and highlighted discussions on social media about economic development in North America including identifying opportunities for investment.

 

Trying to keep up with your Twitter timeline can be a challenge if you follow more than a few hundred people.  It can be a pain point if you are managing a Twitter account following thousands of people.

Listing hundreds of tweets can make you spend time trying to see the forest one tree at a time. Only surfacing the most important signals doesn’t allow you to understand new trends or important dynamics within your community.

People don’t line up in rows unless they have been trained to do so, they naturally organise themselves into networks.  Their conversations are even more networked.  Why should we try to understand their behavior and conversations linearly?  That is why we set out to analyse conversations through social graphs and interest maps.

Nexalogy’s main tools were designed as the industrial strength solution for social data analysis used by BI professionals and social data scientists.  With this App we designed a free tool that does a maximum amount of analysis with a minimum  amount of configuration.

Sign with your Twitter account and the Nexalogy App will organise the concepts mentioned in your timeline into an interest graph.  How every concept is related to the others is displayed as nodes and links.  A node for every concept, with the size of the node proportional to the importance of the concept.   Memes are clustered into groups of concepts for you.

The interest graph is interactive so you can dive right into your data by clicking on any node.  You can easily see beyond the Obvious signalsby exploring the Potential trends and the Long Shots with a one click analysis on the top of the interest map.

In the Analysis tab we list the top concepts and hashtags in your timeline and we let you see all the tweets than mention these concepts by clicking through the pie charts.  At the bottom of the Analysis tab we list the top links shared in your community.

 

In the Connections tab we try to get influencer information you need at a glance, we list :

  • the most active users in your community;
  • a list of Twitter accounts that you should follow based on your social graph;
  • the most solicited members your community; and
  • the most retweeted members

For social discovery purposes, the Nexalogy App will help you see the same results with similar analysis on any search term you choose with the Nexalogy Search option.

We are proud to be teaming up with HootSuite, makers of the market-leading social media management system, to bring this technology to millions of social media professionals and users.  We hope this goes a long way in solving social data overload for you and lets you see the forest through the trees with our cutting edge analysis and technology.

 

 

 

 

 

Google has launched a new, highly relevant capability. Google search now supports the Bacon Number for actors, based on building a small world social network, probably from spidering IMDb or the like. If you type in, for example, ‘bacon number laurence olivier’ you will see this:

Padraig Mac Carron and Ralph Kenna apply social network analysis to old texts — like The Iliad and Beowolf — to see if the relationships between the characters is like real social snetwork. If they are, that lends support for the premise that these tales are about real people, not just some shadows cast on the wall.

Padraig Mac Carron and Ralph Kenna,  The Social Networks of Myths

Social networks have been widely studied in recent years; researchers have looked at the interconnectedness of groups like actors, musicians and co-authors of scientific texts. These networks share similar properties: they are highly connected, small worlds. They are assortative, which means that people tend to associate with people like themselves. And their degree distributions are usually scale-free — a small number of people tend to have lots of friends.

[...]

The myth networks were found to have some of the characteristics, including the small-world property and structural balance (related to the idea that the enemy of my enemy is my friend), typical of real-world networks.

Intentionally fictional narratives like “Harry Potter” also have these properties. However, “The Iliad” is assortative as well — a potential real-life indicator that these fictional networks lack. “Beowulf” is also assortative, but only if the main character, who is very different from the rest, is removed from the network. The “Tain,” [an Irish epic, which many believe to be entirely fictional] like the fictional networks we studied, is disassortative.

These and other features may corroborate scholars’ belief in the narratives’ historical basis: i.e., the societies underlying “The Iliad” and “Beowulf” may have traces of reality, while that of the “Tain” appears more artificial.

But just how fictional is the “Tain”? We looked further into the degree distributions of the social networks — at the frequency of popularity among characters. Like real networks, all three were scale-free, unlike any of the intentionally fictional narratives examined.

In this regard, the majority of the “Tain” and “Beowulf” were similarly realistic — except that in the Irish myth, the top six characters are all unrealistically well connected, giving it both fictional and real characteristics.

But there are 398 other characters in the “Tain,” and when we remove the weakest links (or single, direct encounters) between these characters and the Top 6, the narrative becomes as realistic as “Beowulf” from a social-network view. Perhaps these characters are amalgams of a number of entities that were fused as the narrative was passed down orally.

Our initial results may therefore corroborate existing interpretations of “Beowulf” and “The Iliad” as being at least partly historical. They also signal that the society in the “Tain” may have a similar level of historicity.

So, the next time you watch Troy or Beowulf, or some other movie based on old, old tales, remember that those were real people way beck when, that had friends and enemies, just like we do today.

Imagine a not-too-distant future where machinery — servers, refrigerators, nuclear reactors — are on line, and following each other. They could respond to messages in programmatic ways, and we could use social media analysis tools to discover the ‘sentiment’ of subways and elevators. 

Bruce Sterling called these messaging machines ‘spimes’ (although his concept went a bit further), but it’s clear we are rapidly moving into uncharted territory when machinery may independently who to follow and to talk with:

Alex Williams, How Machines Will Use Social Networks To Gain Identity, Develop Relationships And Make Friends

Social networks serve people as ways to communicate the way we live and work. A machine’s social network can serve similar purposes. The machines can have friends or even families that live in “clusters.” Each machine can learn from the individuals or communities in the collective group. They know when one is sick. They can relate to other machines and the way they feel.

At VMworld,in Monday’s keynote, the attendees saw a demo for how this might work. It shows how a social network populated with machines can spread word to each other. When one host finds an issue, it updates its activity stream. Other hosts and virtual machines will “like,” the update if they are having similar issues.

The VMware example points to an inevitable future. The machines will have a voice. They will communicate in increasingly human-like ways. In the near term, the advancements in the use of social technologies will provide contextual ways to manage data centers. Activity streams serve as the language that people understand. They help translate the interactions between machines so problems can be diagnosed faster.

By treating machines as individuals we can better provide visualizations to orchestrate complex provisioning and management tasks. That is inevitable in a world which requires more simple ways to orchestrate the increasingly dynamic nature for the ways we humans live and work with the machines among us.

Williams starts to creep me out when he — more or less — ascribes feelings to machines. But there is no doubt that machines could message each other, and act on those messages. And of course, they could listen to our messages, and act on them, such as the obvious scenarios of telling our thermostats to turn on the heat. But the not-so-obvious listening behaviors of future machines might be more interesting, like soda machines deciding what soda to order based on analyzing the demographic evidence of occupants in an office building.

That’s a bit more out there than sending the machine a tweet, asking for Izze Grapefruit soda instead of Fanta, and then getting a tweet back two days later from the soda machine — @419ParkAveSSoda1 — telling me that Izze is available in the machine. I notice I am now being followed by 37 soda machines in my neighborhood, and three of them favorited my tweet asking for Izze.

As hurricane Isaac batters New Orleans, seven years after Katrina, it’s worthwhile to ask a few questions about recovery after disasters, specifically why some places never seem to bounce back from disaster. And New Orleans has never really bounced back from Katrina.

New Orleans has the highest per capita murder rate in the US, and has led the country since the 1990s. In 1985, New Orleans had 421 murders, 85.4 per 100,000, a rate than has never been matched elsewhere, and which is more than 10 times the national average.

These are not unrelated facts: they are two sides of the same coin. New Orleans is in the region of the US with the lowest levels of social capital. Social capital is that cultural glue that ties people together, or, in its absence, leaves people as disconnected individuals, without links to each other and without common cause. Pierre Bourdieu defines social capital as

the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition.

And why does New Orleans seem to have such low social capital? Robert Putnam wrote about this issue in his masterwork, Bowling Alone:

Differences among the states on the underlying measures [of social capital] are substantial, with ratios of roughly three to one between high- and low-ranking states. Social trust, for example, ranges from 16 percent in Mississippi to 67 percent in North Dakota. The average number of associational memberships per capita varies from 1.3 in Louisiana and North Carolina to 3.3 in North Dakota. […] Even a cursory glance at America’s social capital resources leads one to ask, “Where in the world did these differences come from from?” Answering that question in detail is a task for another day, but this pattern has deep historical roots. Alexis de Tocqueville, patron saint of contemporary social capitalists, observed precisely the same patterns in his travels in the America of the 1030s, attributing it, at least in part, to patterns of settlement:

As one goes farther south [from New England], one finds a less active municipal life; the township has fewer officials, rights, and duties; the population does not exercise such a direct influence on affairs; the town meetings are less frequent and deal with fewer matters. For this reason the power of the elected official is comparatively greater and that of the voter less; municipal spirit is less awake and less strong…. Most of the immigrants who founded the northwestern states came from New England, and they brought the administrative habits of their old home to the new.

Well-trod paths of migration helped establish regional and local patterns of social capital in contemporary America.

[…]

Still more striking is the spatial correlation between low social capital at the end of the twentieth century and slavery in the first half of the nineteeth century. The more virulent the system of slavery then, the less civic the state today. Slavery was, in fact, a social system designed to destroy social capital among slaves and between slaves and freemen. Well-established networks of reciprocity among the oppressed would have raised the risk of rebellion, and egalitarian bonds of sympathy between slave and free would have undermined the very legitimacy of the system. After emancipation the dominant classes in the South continued to have a strong interest in inhibiting horizontal social networks. It is not happenstance that the lowest levels of community-based social capital are found where a century of plantation slavery was followed by a century of Jim Crow politics. Inequality and social solidarity are deeply incompatible.

So, as I read about the flooding this morning in Tammany Parrish on the outskirts of New Orleans, I thought about the effects of hundreds of years of disrupted social networks, of institutionalized inequality, and the barriers that we erect to stop people from connecting.

New Orleans won’t bounce back, and not because Katrina, Isaac, and other storms are so damaging. New Orleans may linger on as a reminder of a past age, but not one that we should mourn.

Megan Garber at The Atlantic caught my attention with a post about Twitter and the use of swearing:

We know, at this point, how the nation tweets. But what about how the nation swears?

The Ukrainian-based web development firm Vertaline, aiming to answer that question, scanned tweets posted from across 462 specific locations in the U.S. The team then isolated particular phrases from those tweets — one of those phrases being, yep, “fuck you,” which they tracked between July 14 and July 24, 2012. They then created a dynamic heatmap that portrays the density of the F-bomb-laden tweets as they were distributed geographically throughout each day of their date range, measured once per hour.

The screencapped maps above and below track our great nation’s keyword-ed conversation as it played out over a single day: Saturday, July 14. Particularly on the coasts, it seems — and even more particularly in L.A. and NYC – many, many fucks were given.

 

Screen Shot 2012-08-22 at 11.16.11 AM.png

July 14 5:20

The company behind this, Vertaline, provides very minimal controls, but one thing is certain: people in LA and Buffalo say ‘fuck you’ a lot. Must be the song.

New research suggests that people with suicidal tendencies don’t differ from others just because they think about suicidal acts, but because they are not embedded in social ‘neighborhoods’ where their online contacts know each other. It appears that they are isolated in their social graphs:

Spotting Suicidal Tendencies on Social Networks via MIT Technology Review

It turns out the people prone to suicide ideation have about the same number of  friends as the control group. This alone does not seem to be a defining characteristic in the online world, where ‘friends’ are easy to come by.  Neither does age or gender seem to be an identifying chaacteristic, which flies in the face of previous research.

The warning signals are more subtle, say Masuda and co. For example, people prone to suicide ideation are likely to be members of more community groups than the control group. That may be the result of spending longer online and of a desire to want to interact.

But a key indicator seems to be that these people are much less likely to be members of friendship triangles. In other words, they have fewer friends who also friends of each other.  This low density of friendship triangles appears to be a crucial.

So, in terms out outreach, social network analysis — both about regarding content and network context — might be used in the future to help steer people thinking of suicide to help… or maybe we should just introduce their unacquainted friends to each other?