Matt’s main “surprises” and lessons that he offers to grad students in his post include:

Lots of time spent on funding request. I have had a similar experience, because (like Matt) I enjoy working with, and leading, a large group of researchers. Of course, the batting averages are low for funding requests (Matt downplays his success rate but I bet it’s better than average). In my first two years, I submitted 3 NSF proposals, 2 of which were declined and one outstanding (a good sign); I am currently working on two more. Each of these took significant effort, in one case at least (an estimated) two full months of my time. In addition, I submitted a number of smaller-scale proposals, most of them to quick and easy to write, and was fortunate enough to get a Google Research Award (thanks again Goog!), and to be assigned as a faculty mentor to a superstar two-year postdoc Nick Diakopoulos. Together with some other odds and ends (thanks SC&I!) I feel pretty happy after two years regarding the group and resourced I amassed; but the cost on my time is still substantial. On the bright side, as Sam Madden points out in the comments to Matt’s article, some of the grant proposal process is actually helpful in helping me think about future work and research agendas, even if the specific proposal does not get funded.

The job is never done. Even as I write this, I could (and feel that I should!) be editing a paper, or looking at some data, or catching up on email, or working on one of two said proposals. Matt’s admits:

For years I would leave the office in the evening and sit down at my laptop to keep working as soon as I got home.

I can’t say my experience is far from that, although I still insist on taking good vacations. And a 2-year old kid certainly makes for a compelling reason to stop working at any time.

Can’t get to “hack”. True enough, most of the interesting work is delegated to students, as Matt complains that he doesn’t find time to write code. However, that is partially the decision that Matt (and I) knowingly take when we decide to work (and try to fund) a large group of students. Managing fewer or no students might allow more individual research work, which is certainly a path taken by some faculty that skip on the funding requests and the resultant students meetings. However, I am no Ayman, do not miss writing code, and am happy to farm that out to students. I do enjoy thinking about the intellectual and research issues, and often get to do that with the students. I would like to have fewer meetings and less email, but unlike Matt I feel involved enough in the intellectual work, at least so far. Nevertheless, I can’t dive into it like the grad students who indeed “have it good”.

Working with students. Matt writes:

The main reason to be an academic is… to train the next generation.

I see it the same way (the intellectual pursuit is also up there, but it could be claimed that you can perform similar intellectual pursuits in other settings like research labs). The students is why I am in academia, and the advising is by far my favorite activity. From solving someone else’s problems (e.g. a student not sure how do approach X or Y) to, more substantially, showing students a path from a first-year confusion to an experienced researcher that understands how to ask (and answer) research questions, and communicate it effectively. Well, I am clearly not quite there yet having just recently started doing it (and just started funding my first PhD student). But I am enjoying it already. Like Matt, for me it is not just working with the PhDs and Masters students; the undergrads play a big role. I started working with several star undergrads, some of them have never SSH’ed into a server before, most of them have never seen how research is done. Their wide-eyed excitement is an energy source, an inspiration and a cause of constant enjoyment.

So, the bottom line?

It is certainly not for everybody. It remains to be seen if it is even for me.

I will buy that, Matt. At the end of the day, for me, it’s the students, and the freedom to carve my own path. This summer I am lucky enough to be working with my group at SC&I consisting of one postdoc, 2-3 Phd students, 3 Masters students, and 1-3 undergrads (at any given time). With teaching (more on this topic later) out of the way, I spend two full days a week with this gang talking about research, writing papers or grants, having other “good” meetings, or playing Rock Band on our Wii. It’s definitely one of the best work summers I have had, much like my summers at Yahoo! Research Berkeley where we had most of our fantastic interns join in on the fun.

Speaking of the defunct Y!RB, and regarding that path-carving freedom, I feel a lot less constrained in academia compared to industry research. I have had a fantastic experience at Yahoo!, and was lucky to have a great team at the Berkeley lab. However, to start my own project at Yahoo!, that follows my own personal vision, and involved multiple people, would have taken a lot of convincing (and would need to be ultimately tied to corporate agenda). I know Ayman does not agree, so maybe this is just a false sense that I have, that moving a bunch of people towards a vision that I choose and craft is easier in academia. To do that with the students might be, as Matt put it, “the coin of the realm”.

Just a few things went wrong, most of which are Apple’s fault, and some of which are quite annoying.

First, the Mac didn’t recognize the iPhone. Luckily I was clever enough to think of checking for a Mac software update, and sure enough, the only update available was a fix to this bug. +1 point, Apple.

But it got worse once the iPhone was recognized. Soon enough I got this notice right here:

OK, a little scary, and totally wrong (not getting into DRM discussion here) but not so bad as a user experience — the dialog allowed me to continue, give me options, I can live with that (but why didn’t the migration carry forward my authorization?). Anyway, I asked to authorize, only to get another prompt: Something like “sorry, you already have 5 authorized computers”.  This time, I was offered no way out other than acknowledging that lovely, yet curious fact (which 5 machines I had authorized? Ayman certainly didn’t get my permission for any content!). I was too shocked to take a screen grab of that pesky dialog. Still, this wasn’t a big deal, because I knew what to do – de-authorize all my computers (the only one I knew I had authorized was not with me — I migrated from it, see — so I couldn’t just de-authorize it). But that’s wrong, Mr. Jobs. Why would a “normal” (i.e., not 6’8″) user know how to de-authorized their other computers? Instead, I would like to have seen this process:

1. “Hey, it seems like you already reached the maximum number of computers allowed to access your licensed content! Would you like to fix that?”

Options: But of course! / No, I’ll just curl up in the corner and cry

2. “Here are the details of your 5 authorized computers. Which one(s) would you like to de-authorize?”

Options: Select any number of computers to de-authorize.

3. Done!

Another thing that didn’t migrate properly was my Screensaver (although my desktop pictures preference were kept). I guess that’s because in Snow Leopard you need to use iPhoto albums to choose screensaver photos. But why would Desktop background work and screensaver break? Slightly bizarre.

The wifi was also a mild annoyance, forgetting all my preferences (but at least remembering the networks’ credentials for secure networks).

Finally (geek/grad student topic alert), I lost my Latex (MacTex) installation in the migration to the new Mac. I mean, the files were still there but the migration broke a few symbolic links and just tampered with a folder structure enough to make my various Latex editors not find the MacTex installation. MacTex have a several-step solution, but you know me, I take my short cuts (just upgraded to MacTex 2009), which fixed all these issues.

So, Apple could have made this really close a perfect game, but allowed a couple of walks in there late in the innings, just to have Naaman complain. Well, what would I do without them.

Another new tool is called annotations. Already, individual posts show which app someone used to write the post and the date, time and (if users choose to make it public) location. With annotations, software developers will be able to add other material, which Twitter calls metadata, to Twitter posts.

This could significantly expand the amount of information a post includes, beyond its 140 characters, and could enhance the way Twitter is used.

Posts could include the name of the restaurant where a post was written and its star rating on Yelp, for instance. Then, someone could find Twitter posts about restaurants nearby with five stars. Or developers could add a way to make a payment and purchase, so retailers could sell items from within a post.

Twitter does not know what developers will decide to do with the tool, said Ryan Sarver, who manages the Twitter platform. “The underlying idea is think big, push yourself.”

Sounds very close to what I asked for. Of course, there are the Machine-tag skeptics but they just need a good moment alone with Aaron Cope, Clay Shirky and a machete. Free the information hierarchies!

I have been showing both these images in my talk for a while now — can’t seem to get sick of them, even if my audience might just be…

I’ve also been talking for a while on how we can use the aggregate contributions on Flickr to mark boundaries of geographical objects, such as, say, neighborhoods, using all the photos tagged with a neighborhood name. Talk is cheap, but the smart people at Flickr not only figured out how to do it (with slightly different data than tags) but also released the data and the source code for anyone to use. Blame Aaron Cope and Rev Dan Catt.

Well, here’s the thing: turns our a famous scholar also beat Flickr to it, some 40 years ago. Kevin Lynch, in his groundbreaking essay/book The Image of the City, collected people’s descriptions and hand-drawn maps of three cities (Boston shown here, also Jersey City and downtown LA). In one study, he extracted the “maximum boundaries” for each neighborhood as drawn by all the interviewees, and plotted them on the map.

Here are the automatically-created, Flickr-based map of Boston Neighborhoods, visualized using the excellent Tom Taylor’s Neighborhood Boundaries, and Lynch’s maximum boundaries of neighborhoods in the same area.

I have pre-ordered Milgram’s book of essays, to arrive in February. Might as well find out what’s there before we re-invent something else!

So now, after teaching the first session of that class, let me try again. The following definition will try to broadly scope the topic as described in my Social Media class. But I also believe that this would make a good working definition of this widely-and-wildly-used phrase.

In this definition, I try to follow closely the original meaning of both “social” and “media”. Media is defined as:

the main means of mass communication (esp. television, radio, newspapers, and the Internet) regarded collectively. (Apple Dictionary)

And Social:

needing companionship and therefore best suited to living in communities “we are social beings as well as individuals.

These definitions are echoed in the following, although did not directly dictate it.  Social media, then, is any media that supports these two characteristics:

• Posting of lasting content in public/semi public settings within an established service or system.
• Visible and durable identity, published profile, and recognized contribution.

This definition would then include Facebook, Flickr, Delicious, MySpace, Yelp, Vimeo, Last.fm, Twitter, Dogster, YouTube and their many, many likes.

The definition does not include purposely excludes old media services that allow for comments from users (no durable identity); Wikipedia (no “recognized contribution” that is easily associated with a user); or say, mobile-social applications (no posting of content => not a media!). The definition also does not newsgroups and discussion forums (no published profile, no expectation of lasting content). And it does not include communication services like IM and email that are not public, not even semi-public in nature.

Does this make sense?

A recent article in the PNAS was quoted in quite a number of media outlets (Hindustan Times gave the Masters student responsible a PhD as well as professorship). From the article, Cognitive control in media multitaskers, by (the formidable team of) Eyal Ophir (get a Web page!), Cliff Nass and Anthony Wagner:

Results showed that heavy media multitaskers are more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory.

Heavy multimedia multitaskers (HMMs) are identified by a survey about media use, and compared to low multimedia multitaskers (one standard deviation over vs. under the mean of the index). The paper compared HMMs (not sure they are aware of the other meaning of the term) and LMMs on a number of tasks, finding that:

individuals who frequently use multiple media approach fundamental information processing activities differently than do those who consume multiple media streams much less frequently: their breadth-biased media consumption behavior is indeed mirrored by breadth-biased cognitive control.

In other words, those who multitask are not effective multitaskers – it’s the opposite. Of course, there are still outstanding questions:

• Causality: what is the direction of influence? Do HMMs (I still find it hard to use this acronym) tend to breadth-biased consumption of media because of their distraction?
• Index validation: how robust is the survey and metric created to capture the “media multitasking” index? Do survey participants’ self-reports actually attest to their real behavior, and does the survey really capture “multitasking” or something else? The authors note that Media multitasking as measured was correlated with total hours of media use — maybe that’s what was measured?
• What other factors are in play? As the participants were all Stanford students, I do not expect major age, economic or education gaps; the also authors tested for differences in a number of dimensions (SAT scores, performance on a creativity task, ratings on extraversion, agreeableness, conscientiousness, neuroticism, and openness and others) and found no significant differences between HMMs (grrrr, acronym!) and LMMs. Does this cover it or is there any other factor that will help explain the differences?

In any case, interesting study — I am looking forward to the follow up work. And now, off to another media!

While both are great, Twitter will only be ready to take over the web when they official adopt the next feature in line: support for adult-material spammers to add everybody as their follower at the same time.

Or, more importantly, hashtags.

A first-class support of hashtags will be the final nail in the coffin of Twitter taking over content everywhere (the Web, the world, the old media, TV, everything). Hashtags support would not only mean that a user can flag the topic of their post (#iran), saving a few characters on the way. Solid support for hashtags would mean that any user would be able to semantically tie their tweets to any type of object, virtual or real. Couple that with the flexibility of the Twitter system, and you have a platform where anyone can “attach a note” to anything, anywhere, anytime.

Examples? But of course. My tweet is about Society Coffee in Harlem. My tweet is about Sony Playstation III. It’s about the first episode of the Mad Men latest season. My tweet is about Rutgers SC&I. My tweet is about the web page of Rutgers SC&I. My tweet is about the New York Times article about Retweets. My tweet is about Ayman Shamma. My tweet is about Calexico.

Wait, how would that be different than just adding the hashtags in the text (e.g., #societycoffee)?. Well, Twitter people are smart. And they are friends of the good folks at Flickr. They will surely support Machine tags a-la Aaron‘s.

Machine tags will allow a much more robust (read: semantic) connection between the hashtag and the object discussed. I will still be saying #AymanShamma, but the system will store #facebook:user=111111 (or #twitter:user=22222). I will be saying “Calexico live in Barbi Tel Aviv” and the system will store #lastfm:event=33333 (Flickr’s last.fm machine tag now sports 1.2 million photos with a last.fm machine tag). Similarly, whether it’s a product name, a web page, a school name… a strong Twitter and client implementations can help users assign exact semantics (when they so desire) to any post.

Especially with location.

Context aware Twitter clients are a step away of being able to provide the users with the power to comment on anything, anywhere. I am pulling my iPhone out in a restaurant. My Twitter clients knows where I am, and gets IDs of nearby restaurants from Yelp. The client lets me select the restaurant I am in (or guesses it automatically based on the text and location). My post is now tied to the semantic object that is that restaurant (identified by Yelp ID, #yelp:biz=society-coffee-new-york-2) instead of just matching the text of the restaurant name (“Society Coffee” would not help much in matching and search tasks).

The Twitter API would surely allow other players to “read” all this content. Companies could show tweets about their products on the product page (or even ask users to tweet with #REI:productid=444444). If you are in a live event, a big screen can show all the content tagged #lastfm:event=555555 (which will be easy for any user to add to their post using their location- or calender-aware client). And more.

“If you liked this painting tweet #moma:paintingid=6666666″. We might see a lot more of these in the future. Twitter will bring on the object web. Just hash it out already!

p.s. Of course, our ZoneTag already did all these things (on Flickr) by 2007.

But in the open teaching tradition I started last January, I am going to ask the one dear reader of this blog (it’s not Ayman, he just writes it) for input. What do you think a social media class should include? Try to think about it for a minute before looking at our tentative plan for the class, below. What did you hit that we didn’t?

Of course, two questions are immediately raised: 1) what is social media and 2) what is the target audience for the class. Let me start with the second, which is easier to answer. We target PhD and Masters students in various programs including Computer Science, Information Science and Communication (we even have a business school student registered). Letting both PhD and Masters students take the class means we need to balance theory/research and practical learnings that the Masters students can take with them to the workplace. Also, the interdisciplinary approach and audience means we will handle material from the social sciences, HCI and design, as well as computer science and information science topics.  One last thing to know: the students will form interdisciplinary teams to create/design a social media application (e.g. a Facebook app).

So, what is social media? Well, as you can see below it is the topic of the first session, so I am not going to give the full story here. In short, we see social media in a new (an emerging) Information and Communication Technology (ICT) that allows people to communicate in a public or semi-public manner, with emphasis on the personal identity of contributors and social connections. I will keep it short here so just a few positive and negative examples: Facebook, Twitter, Flickr, MySpace are social media. Wikipedia, comments on New York Times articles, Instant Messenger and Newsgroups are NOT social media. Let’s argue about that definition later…

So, what would you teach that’s important to understand this emerging ICT? What are the key readings that are not to be missed? Here’s what we have for now, without the readings. Feel free to suggest your favorite reading on each topic, as well!

• What is Social Media: introduction, definition and examples.
• Communities and social networks: Concept of communities, offline and online, how this concept is shifting; what are social networks (i.e. ties between people) and what to they enable.
• Social network models and structure: online social networks, analysis of social networks, structure of networks, ego-centric network view, etc.
• Open Platforms: “Web 2.0″, “the Web of data“, APIs – some idea of what can be built on top of existing social media applications.
• HCI and Design: introduction to the design process with emphasis on Web and social applications.
• Motivation and adoption: when do people adopt certain social media services, and why do they contribute to them? In other words, what is the motivation of people to join and stay active on social media sites?
• Social media across cultures: a cross-cultural look at the social media phenomena.
• Mobile-social and Social Media for Good: with a very special guest speaker!
• Data Addicts: Data Collection, Analysis, and Visualization (for research or application purposes) of social media data.
• Study Design and Data Analysis: introduction to research on social media services; how to design studies and analyze the data.
• Social Information Design: an information-centric approach to social media; what are the different information factors in play (yes, this is where we talk about tagging).
• Privacy, Legal Issues, Copyright, and IRB.
• Economics of social media.

That’s it for now. As you can see, we plan jump from the theoretic, to the practical, to the research-y topics, hopefully making for a good mix. What did we leave out? What should we leave out? Your input is welcome, or as Dr. Boase would say, “we will try our darnedest to include suggestions, but may not be able to include all of them”.

I am sure Michael, a former NSF director, doesn’t completely believe in that, but I also believe that this calculation overlooked a very important value-generating factor: idea diffusion from the academic to the private sector.

You know how sometimes you have a great idea or insight, only to discover that somebody else already had that idea? Even worse, you know how this happens and you realize that you actually read that paper where the idea was described but forgot all about it? (yes, Ayman, this happens to people my age, you’ll see when you get there).

My thesis is that while not all NSF proposals get funded, all of them are reviewed and evaluated by panels that include many researchers in  companies. The ideas in these proposals stick in the minds of the readers, and could very well be unconsciously adapted or used later (I am not saying anyone is out to steal the academics’ ideas!).

This is why I find Google’s Faculty Research Awards an entirely good idea, both for the faculty and for Google. In particular, the open nature of the awards program (not specifically tied to current Google products or data, for one) is a key feature. Yes, G will get a lot of submissions and will spend valuable time reviewing each and every one of them. Obviously, the funded proposals will benefit Google as they create a direct link between the researcher and a Google person that will learn about the findings. But even the mere act of having all the world’s researchers sending Google their ideas and suggestions is bound to leave some trace in Googler’s minds. Google thus increases the funnel of innovation and ideation (I hate that word) to collect input way beyond its own engineers and employees.

Now, if they end up supporting my proposal, that would prove that they are even more brilliant!

Oh, and also that I tried to introduce the opportunity of social media data to the smart people in the SSTD community. Especially as Twitter is rumored to add location data, we are about to witness a significant new information system with social, spatial and temporal data all at the same time and in a never-before-seen scale. The opportunities are, as you might guess, endless.