Chez Curieux Gauvin
Pour Noël, je veux pouvoir filtrer me sources d’info et les mentions de mots clés selon les participants et les auteurs. Quand je fais une recherche, je veux qu’on me sorte des conversations se déroulant entre des gens qui correspondent à certain critères que j’aurai défini selon la recherche : domaine d’expertise, degré de proximité, géo-location, niveau d’influence.
Merci Internet
PS : tell me about you
As the web enters a more user-centric era where conversations can be gold mines, you will soon need the ability to filter content and news based on the influence level of the messenger to cut through the noise. If you are looking for good blogs or discussions about Django (my current framework of development), you should be able to see any post/tweets written or commented by Jacob Kaplan-Moss (one of the lead developer) at the top of your search results.
To do that, you first need to determine who has some influence online around that topic. There is currently multiple web services to evaluate user ranking, and I’ve reviewed some here. Klout is another of those services and a pretty popular one. Main problem with most of them is that they usually have no concept of relevancy : they evaluate your reach, influence, activity and other parameters as a generic score, without any relation to a topic or domain of influence.
I’ve just performed a search for ‘Django’ in Klout’s topics search box. Number one influencer on this somewhat niche topic is … Tara Hunt aka MissRogue. For those who might not know here, MissRogue is a successful author/marketing consultant/entrepreneur with a somewhat decent audience of … 29757 (Dec 1st 2009)followers on Twitter. She probably knows more about Django than the average person, but I would have expected Jacob Kaplan-Moss or some of the bloggers in my Django links collections to be slightly above her in such ranking…
klout django influencers
What happened is probably that she tweeted that keyword several days ago and the Klout engine pick it up, putting here in their Django topic basket and by looking at the outstanding number of followers she has, promoted her to the top of the list. A more complete analyze of her tweets would have been enough to avoid this error. Further analysis of shared link would be even better, but slightly more complex.
User Ranking methods and technology will most certainly become ubiquitous in a near future and evolve into something solid and relevant enough to be used as a content and conversation filter, as well as for targeted advertising purposes. A great example of the relevancy concept can be found in Traackr from which I just got a demo. They still do some manual intervention for quality assurance, but they have included a relevancy score related to the queried domain, as I would expect. We talked a little bit about their roadmap and they seems to have some pretty interesting stuff coming up.
Are you planning to use some kind of User Ranking in your applications or web strategies ? How ? What are you expecting from such tools ?
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When trying to identify early influencers and pool of knowledge around a certain topic or industry, finding early poster/commenter is one simple trick often used. Not very complex, but it often leads to other links and users and help shape how the information started to spread. Backtweets is great to see who shared a link on Twitter (despite the limited timeframe available), but Delicious gives a very different type of results and the ability to research much older content to see who picked up on it first.
I am (still…) an heavy user of delicious. By clicking on the number of persons who shared a link in delicious, you access the bookmark history page. On top of this page,the ID of the first user to share this link is displayed and the date when he shared it. It also gives you a basic timeline to look at to see when the url popularity took off. Despite a clear drop in popularity, there’s quite a bit to be said about the usefulness of the data in delicious. Something to try…
Note : Because Delicious development has been pretty stale (cough) lately, I find myself using Diigo more and more. My favourite feature is the ability to create private groups to share links with clients and partners on specific topics and projects. Main problem is that I still search for links in delicious and the auto-tagging feature is not as relevant as there is less links shared on Diigo…
I’ve also recently found out about a new startup in Montreal called Wajam offering the same kind of bookmarking tool, but haven’t had the opportunity to test it yet.
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I’d be hard pressed to guess if social media monitoring tools have finally crossed the chasm but they’ve clearly been riding a solid buzz for the last 18 months. Looking at the client list of Radian6, Sysomos or ScoutLabs you soon realize that many major brands have jumped on the bandwagon to be able to monitor the buzz and sentiment that their brand, products and competitors generate.
Some voices are questioning the real value and possible ROI of these platforms, sometimes in opposition with free solutions. And it totally make sense when monthly fees easily range in the 500-1000$/month for the main solutions with a decent set-up.
One of these, entititled “The problems with Social Media Monitoring technologies“, seems to have decent web findability as it was sent to me a couple times by friends and clients in the past weeks. I’ll take a moment to go over these particular points one by one.
1. The technology is fairly stupid. It either don’t do what it says on the tin or do it quite badly. It is supposed to be simple: based on keyword configuration, the software scrolls the social web and collect mentions of your keyword(s). It then supposes to analyse themes and influence ranking and sentiment etc, which it does with limited accuracy . For example, will pick up any header, ad sense or footer mentions of your keywords even if it’s in the totally irrelevant context. If your brand name is pretty generic you are in deep sh*t. Hours of configuration and exclusions awaiting you.
You have the same problem when you search with Google. Building up good search queries takes time, analysis, trial & error and a good understanding of the brand, product and industry . Nobody said social media monitoring would be easy or obvious. The web is getting noisier everyday and good queries are more valuable than ever.
Unreliable data. The most important thing to understand is that the software simply provides you with piles of data. Before you can extract anything meaningful from this data you have to go through hours and hours of spam filtering which can be very tedious if you are dealing with 1000s of mentions every week/month. In some occasions I had over 50% irrelevant data coming through my dashboard. Additionally, the spiders cannot access all social spaces and sometimes the most important conversations are blocked.
There’s a clear need to be able to classified results by social engagement, source, language, sentiment and geo-demographics. There’s spam in Google results as well obviously and you need good tools and techniques to cut through the noise.
Sentiment analysis is flawed. Again, this is part of the limitation of the technology. The software analyses keywords, not human emotions and, on average, the software gets it wrong 30% of the data because human emotions are subtle and complex and not easily categorised by software – we are not there yet.
I agree. And one of the reason is that we are usually measuring the message instead of the effect. Sentiment analysis should be mainly used as an extra filter. 60-70% reliability is better than nothing and probablu much higher for tweets.
Good monitoring tools allows for user to overwrite the results of sentiment analysis. Results needs to be reviewed, but it’s still much faster than evaluating everything manually.
Region specific data: for global brands, social media have very strong global element as well as clear regional bent (forums, blogs, networks etc). This is tricky especially if you are working with a regional client (e.g Huggies UK). Problem is for the software it’s not about where you are but which domain are you using. So reliable geo / regional analysis is, in many cases imposible to carry or not complete so need to be complemented with manual search.
True, Twitter localization is only based on what the user enters as location in but will soon be changed by the new API opt-in feature.
The ability to drill down the query with localization-related keyword helps obviously.
Influence analysis is flawed. Well, the concept of influenced is flawed so of course technologies of measuring it are flawed as well. Similar to sentiment, the technology is just not as clever as they want you to believe. It is based either on bogus metrics or just irrelevant, obsolete ranks .
Agrred, most implementation are. That’s why I always ask before evaluating or using. Sysomos has their own Postrank-style ranking. Social Mention (free) as added PostRank to their results.
If I were to design a full platform, I’d be using Postrank to rank results.
Time consuming. Because all of the above, the reality is that while thess companies provide you with piles of data and funky visualisations the profound unreliability of the software means you have to sit for hours and days and configure the dashboard, refine the data, correct the scores, filter the spam, get rid of irrelevant data AND THEN, AND ONLY THEN you can start making some meaningful analysis.
It does take quite a lot of time to set-up and it’s not easy. After a lengthy initial process, the work involved somewhat decrease but you will still have to filter out some spam.
The analysis will always take time. That’s why many brands work with analysts to help them make sense of it.
Price. This varies significantly but the fact is that you pay just for the data and license fees to use the software. For the level of service you don’t get value for your money.
I’d expect to have access to an API to integrate with client workflow and platforms or to customize views and results, the ability to query on past period and modify queries at any time.
Conclusion
Social Media Monitoring platforms are not be-all end-all solutions, more of a building block of brand’s modern tool set for effective market research, marketing and customer support. They require investment both in time and money and more often than not, some support from people who have done it before and are comfortable using these particular tools.
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Ranking social media users can be seen as an ego trip, but with communication and information channels growing and evolving so fast, it simply make sense to use a somewhat automated way to filter out users based on their influence. While PostRank gives me a nice way to evaluate social engagement around content, many apps are starting to express the need to use a similar filter to evaluate Twitter users by interest and activity.
Getting a score for a user is not so useful in itself, but is (we’ll be) a building block and filter-classifier of many upcoming tools and services.
I haven’t yet found a tool that fully satisify my needs but here’s the best of what I’ve tried so far.
The Tested Ranking Tools
The References
To perform a real test, I needed some kind of references, so I used the following profiles:
The Normal Guy
@PhilGo20 : My own profile. I consider myself a fairly active Twitter user, with an ok following (around 560) and a close to 1:1 follower/following ratio. I retweet, been retweeted a couple times and am mentionned a couple times a week in reply.
The VIP
@SebProvencher : Seb is a Praized.com co-founder (Mtl startup) and Product Development Manager. I like his tweets and read all of them (thxs for TweetDeck group feature). He has close to 1500 followers, but he’s only following 480 persons, giving him what is seen as an excellent follower/following ratio.
The Popular
@Caterina : Caterina is Cofounder of Flickr and Hunch and a social media startup star. She has over 9000 followers but just over 200 tweets.
The Not-So Active
@PhilipBoum : Philippe is one of the NOFOLO/Percute Technologies guy in Quebec City, involved in the business side of these web services small companies. I don’t know if he’s using an automated follow back script but he has over 1000 followers with only 14 tweets and have not tweetted once in the last month. I am guessing he has a life outside Twitter, and that’s good !
The Spammer
@entrepreneur949: Sorry if you read this and you are not, but by your tweets and your link, if I were to design a spam tool for Twitter, you would be targeted.
The Inactive (used as a reference for the test)
@philippegauvin : I am squatting my own name domain. Only 2 tweets redirecting to my real account. 0 followers / 0 following.
Each reference is pretty self-explanatory, except the difference between Caterina and SebProvencher. I wanted to have both because I think they have different type of influence. To me most of SebProvencher influence comes from his activity on Twitter while Caterina’s comes from who she is. Without being very active, she has an important following. Praized is not know on the street while Flickr is (pick your street).
The Results (on August 27th 2009)
Retweetrank (0-100 percentile)
Twitter Grader (0-100 I suspect it’s a percentile)
Twitterank (not normalized, 0-200+)
First, despite the usefulness of TwitterGrader and some well thought features (I like the tag cloud), it is no yet usable in my sense as it rank inactive and spam account way to high.
Retweetrank does a pretty good job, but it’s very harsh if you haven’t tweetted in the last month. I would think PhilipBoum deserves better than 0. Having read the way they rank users, it seems to be only looking at the last month of activity. There is pros and cons to that approach in my mind. It does a very good job at filtering out Spam account though. What I am wondering is this : Could only one spam supporter account retweeting the main account be sufficient to put Retweetrank at wrong ? Have not tested it yet. Also, it does not really separate significantly me, Caterina, and SebProvencher. That’s why you cannot be using a percentile as an actual rank.
TwitterRank does a good job too, but also harsh on PhilipBoum account. Maybe that’s the way it should be, but maybe the guy took a month off Twitter ! On the other hand, this is the only tool, that seems to normalize the value (not using the percentile as a rank) and the only one that shows the difference between Caterina and SebProvencher’s influence.
Conclusion
I think TwitterRank is doing the best job by far here and their simple API is also a big plus. There’s is going to be a large number of ranking tool for social media users coming up in the next months, so it’s futile to bet on the future of this service but the need for it is obvious to me. Mash it !
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Stéphane Guérin, que je connais heureusement hors Twitter, a publié un billet très commenté ce matin sur le “snobisme à la Twitter” où il explique avoir été confronté à l’expression et où il expose sa philosophie face à la question “suivre ou pas suivre”.
Je respecte sa façon de voir (même si je suis super déçu qu’il ne me suive toujours pas, ça doit être les tweets sur le bécik…) et moi-même je ne suis pas non plus tout ceux qui me suivent. Par contre, je remarque que beaucoup de commentateurs et Steph lui-même semble avoir un problème commun : comment suivre un nombre grandissant de personnes sans manquer les tweets importants et passer sa journée là dessus. Ma réponse à une question que je me suis posé : utiliser les groupes dans TweetDeck.
C’est du connu pour un paquet de monde, mais pour les autres, voici comment je m’en sers. Le problème est qu’on ne donne
twitter_tip
pas la même importance à tous ceux qu’on suit: il y a certains dont on ne veut rien manquer ou presque et d’autre qu’on voudrait lire une fois de temps en temps en diagonale.
Je me suis créé 6 groupes, selon mes intérêts bien sûr :
Je ne manque rien des 3 premiers et pas grand chose des 3 derniers. Par contre, une grande partie des comptes que je suis ne sont dans aucun groupe et re retrouvent dans la colonne “All friends” que je check une fois de temps en temps, rapidement et en diagonale. Si je vois que quelqu’un tweet constatemment des trucs sans intérêt, je l’enlève bien sûr. Si au contraire, c’est toujours pertinent et que je le retweet, il risque d’avoir un upgrade dans un des groupes.
Bref rien de bien compliqué, mais qui permet de suivre un plus grand nombre de personnes et de bénéficier de leur tweets de temps à autre sans manquer les VIP 
Reste à espérer que Steph Guérin me suive un jour, mais parti de même, je fais une croix là-dessus
En feuilletant le Voir, j’ai appris la bonne nouvelle que UnderWorld, un des bands les plus légendaires de la scène électronique sera de passage au Métropolis le 12 août ! Que vous soyez un fan fini ou pas, si vous avez un tant soit peu d’appréciation pour la musique électronique, le gros son (et le party), la question se pose pas, vous devez être là le 12 août ! Si vous êtes pas sur de savoir c’est qui, pensez à la grosse toune de Trainspotting, c’est eux. C’est le genre de gros booking qui manque à Osheaga selon moi pour qu’on se pose pas de question sur leur événement, mais ça c’est une autre histoire.
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On me demande souvent à quoi sert Twitter et je suis bien d’accord que c’est pas toujours clair. Mais s’il y a une utilité majeur à Twitter pour le commun des mortels, c’est la recherche en temps réel avec Twitter Search. Pour une recherche sur ce show en particulier (underwordl montreal), Twitter me redonne 5 résultats, dont 3 sont des commentaires de fan en extase devant la nouvelle qui ont posté dans les 3-5 derniers jours. De son côté, Google ne me retourne aucun résultat lié à l’événement en première page (surtout des résultats pour la shop de skate).
Twitter m’apprend de surcroît que The Orb (un non moins mythique groupe britannique de la scène électronique) sera à Montréal dans le cadre du festival de Jazz le 8 juillet au Club Soda. Et c’est exactement le genre d’info que je recherche.
La même recherche sur Social Mention, engine de recherche “social media” d’un startup d’Ottawa m’apprend qu’un blog de Montréal rapporte la même nouvelle.
Si ce n’est pas un excellent exemple de la supériorité de Twitter Search dans certains contextes…
Maintenant, j’ai 2 paires de billet à acheter