This post is the first of a two part article addressing the question: How do you know if your influence score is correct? Today, I won’t actually answ
Today we illustrated the predictive validation framework through an example of predicting Apple’s stock price. This predictive validation framework is very general and can be used to validate any models (or algorithms).
To properly validate a model (any model), we must be able to compare the model’s predicted outcome with an independent measure of the outcome. Here, the outcome can be literally anything (e.g. stock price, influence, weather, earthquake, etc.). I’d like to re-emphasize the importance of having anindependent measure that is truly independent of the model. That means you cannot use this measure anywhere in your model. Otherwise, the validation procedure will be confounded by circular reasoning.
Alright, now you know how to validate any model, next time we shall apply this framework to analyze the models that influence vendors use to score people’s influence. And we will be able to answer the question posed at the beginning of this post: How do you know if your influence score is correct?
Stay tuned... Have a warm and relaxing Thanksgiving... And see you next time.
Last time we took a quick peek at the history of SEO, and we saw that influence engine optimization (IEO) is an inevitable consequence of scoring peop
If you look back at the Webster’s definition of influence that I quoted earlier (seeWhat is Influence, Really? – No Carrots, No Sticks, No Annoyance, No Tricks), it says: “The power or capacity of causing an effect in indirect or intangible ways : sway.” So, whatever the effects are (i.e. the changes in thought or behavior), it must be caused through “indirect or intangible ways.”
What does this mean? It is actually a subtle but important point that many discussions about influence have overlooked. It means that influence shouldn’t be something that people can affect directly and easily. This is the key to fixing the influence irony. Because people would otherwise be able to game the system and raise their own influence score without doing anything influential.
Why is the “Influence Irony” so Challenging?
To mitigate the gaming of influence, vendors must ensure that people can’t directly affect their influence. However, influence should be something that people can affect through their actions. Otherwise people’s influence will never change. Soinfluence is something that people can affect, but not directly or easily.
What is an Adaptive Influence Model?
First let me clarify the difference between an adaptive model vs. an adaptive score:
An adaptive influence score means that the score is adaptive to the behavior of the individual. Most influence algorithms already do this by incorporating a timing factor in their influence scoring algorithm. This is easy, because when an individual’s behaviors changes, his social media activity data will also change to reflect his behaviors. Since each person’s influence score is computed from his social media activity data, the score will obviously change, because the input data to the model has changedAn adaptive influence model means the algorithm that calculates the score will change and adapt to the behaviors of thepopulation. Most influence vendors do not do this, because it is much harder. However, it is this evolution of the model that is going to fix the influence irony. Let’s take a look at what this means in greater detail
« Fachosphère », « conservatosphère » ou « réacosphère » : les mots ne manquent pas. Mais aucun ne permet de rendre compte de la place occupée par l’extrême droite sur le Web français, car tous recouvrent des courants et des communautés bien différentes. Pour établir une cartographie synthétique, nous avons passé au crible 377 sites et blogs situés à la droite de la droite ou à sa périphérie.
ATTENTION : tous les blogs cités dans cette carte ne peuvent pas nécessairement être considérés comme étant à l’extrême droite de l’échiquier politique. Ils y figurent néanmoins car ils font référence auprès d’une communauté qui, elle, fait partie de la droite de la droite (voir la discussion dans les commentaires au bas de l’article). Il s’agit donc plus d’une carte représentant des réseaux d’influence que des communautés figées. ...
Founded over a decade ago, Technorati Media has grown into one of the largest social media ad networks bringing top brands and valuable influencers together at scale. With an advertising reach of approximately 130 million US unique users/month, sit in the middle of an incredible social media nexus and have long believed that our data, relationships and unique perspective are important elements for us to share.
With this in mind, we present to you our 2013 Digital Influence Report, which replaces our historical State of the Blogosphere and expands the concept of all things social. We hope you’ll benefit from the valuable insights culled from surveys that included over 6,000 influencers, 1,200 consumers and 150 top brand marketers.
The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, but also by directly triggering each other through contagion. Although credit default swaps have radically altered the dynamics of contagion for more than a decade, models quantifying their impact on systemic risk are still missing. Here, we examine contagion through credit default swaps in a stylized economic network of corporates and financial institutions
Derivatives and credit contagion in interconnected networks
In humans, the distribution of yawn contagion is shaped by social closeness with strongly bonded pairs showing higher levels of contagion than weakly bonded pairs. This ethological finding led the authors to hypothesize that the phenomenon of yawn contagion may be the result of certain empathic abilities, although in their most basal form. Here, for the first time, we show the capacity of bonobos (Pan paniscus) to respond to yawns of conspecifics.... The importance of social bonding in shaping yawn contagion in bonobos, as it occurs in humans, is consistent with the hypothesis that empathy may play a role in the modulation of this phenomenon in both species.
Savez-vous quelle image vous renvoyez sur Internet ? Qui peut voir vos photos, vos statuts, vos données personnelles? Grâce à ce test vous saurez si vous gérez correctement toutes ces informations sur Internet.
8 questions vont vous êtes posées et vous serez chronomètré. Plus vous serez honnête dans vos réponses, plus le résultat du test sera proche de la réalité.
In my previous posts, I defined influence and discussed why brands don’t seem to understand digital influence . Today, we are ready to talk about t
he Big Missing Link of Influence
So when does real influence take place? To be absolutely rigorous and correct, no one knows for sure. But we do know when influence will occur with high probability. That is when the influencer’s potential to influence is aligned with the influencee’s potential to be influenced.
Because all that influence vendors have is a score that is indicative of the influencer’s potential to influence, there is a missing link in the influence industry today, and it is the influencee’s capacity to be influence. A couple years ago when I introduced a simple influence model, I found four categories of attributes that characterize the influencees’ likelihood to be influenced:
Today, several influence vendors have implemented timing and channel alignment, and to a much lesser extent relevance. However, they have implemented these as attributes of an influencer, whereas they should be attributes of the influencee. For example, influence vendors treat timing as an influencer attribute, which indicates when the influencer’s frequency of communication changes. But the timing that I talk about is an attribute of the influencees, and it characterizes the temporal window within which the influencees are susceptible to being influenced.
No wonder, brands don’t get influence, because I don’t even think the influence vendors get influence. In reality, an influencer’s potential to influence has little correlation with his frequency of communication.
For example, President Obama’s potential to influence is the same regardless of whether he communicates or not. His actual influence (i.e. how many people he actually influenced) does change depending on how much he communicates. But influence vendors do not measure actual influence; they can only estimate someone’s potential to influence. By including the timing factor as an attribute of the influencers just tells me that even the influence vendors do not understand the difference between the potential to influence and real influence.
Most influence vendors focus on measuring the social capital of the influencer, but real influence occurs when a change in thought or behavior is produced in the influencee (i.e. person being influenced)This is the missing link of influence: the link from the “potential to influence” to the “potential to be influenced.” Real influence can only occur when there is an alignment between these two. In fact, this is the minimum required state
Influencer marketing has huge potential. But as an industry we are far from realizing this potential. Partly is because influence is a challenging concept that involves much more than just the influencers themselves. Moreover, there are many big data and analytics challenges in accurately estimating someone’s potential to influence. But first, influence vendors need to start incorporating more attributes of the influencees into their model in order to improve the accuracy of their influence score.
Next time, let’s dig deeper into the algorithms that score influence. In the meantime, if you also do research on digital influence, I’d be happy to discuss your findings here.
'If you've agreed on Klout as a screening tool for a social media campaign, what happens if, two weeks in, your client's influencers just can't be found on Twitter?
You'll be forced to scrape the bottom of the Klout barrel for low-score influencers and – if you're an honest PR person – forced to return sheepishly to your client to have the conversation you should have had to begin with: The one where you explain that Klout isn't the definitive social media influence metric of our time.'
[AS: An enjoyable piece, marred by the author's land-grabbing claim that 'how influence is measured should be the domain of the PR pro, not a tech startup', which made me chuckle.]
Certain individuals are more effective than others at using individual experience to impact group behavior. Here, we tested whether pre-training of zebrafish that are at the focal central of social group dynamics (“Key” fish) has a stronger positive impact on group performance than does pre-training of less central (“Non-Key”) fish. We used very short observation periods and social network statistics to identify Key and Non-Key individuals, trained these fish to respond to an aversive stimulus, and then measured group performance after returning these now-experienced fish to a social setting. Although Key and Non-Key fish evaded the stimulus equally quickly as individuals, groups with experienced Key fish escaped the aversive stimulus more quickly than did groups with experienced Non-Key fish. The impact depended on genetic background: PN zebrafish on the social extremes (more often males) influenced the group's baseline response to the aversive stimulus, whereas experienced Scientific Hatcheries' zebrafish (both males and females) influenced the change in response over repeated trials. These results suggest that social roles are an important feature of information transfer across a group, and set the stage for future research into the genetic and evolutionary basis of social learning.
The phenomenon of social contagion—that information, ideas, and even behaviors can spread through networks of people the way that infectious diseases do—is both intuitively appealing and potentially powerful.
It appeals to our intuition for two reasons. First, it is obviously true that people are influenced by one another. Reflecting on our individual experience of life, it is easy to recall any number of instances in which we have been influenced, whether by our parents, our teachers, our coworkers, or our friends, and corresponding instances when we have influenced them. And second, once you accept that one person can influence another, it follows logically that that person can influence yet another person, who can in turn influence another person, and so on. Influence, that is, can spread.