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I agree with Professor Wolff that lecturing in class like speaking on camera require training, practice and preparation. I also agree that the rules for speaking on camera, such as head movement, body movement (no shifting, no balancing and even more no rotating on a chair please) or the short, clear sentences are very different from the engage your audience, show enthusiasm and make eye contact with your class rules for public speaking. However, I believe that in raising his question and stating his confusion, he forgot (perhaps on purpose) to consider the environment (or the medium) of the lecture delivery, the time and the audience.

The rules that he states sound to me very much like media training where emphasis is put on broadcast (live or recorded) appearances. Speaking for television is different from delivering a lecture. The camera usually is focused on the speaking subject giving little information on the context and surroundings. I do agree that in these situations the movements should be more reserved and the speech planned to contain several key messages.

However, when we are dealing with a recording of the lecture which is delivered live at some point to a given audience, I believe the rules change.

If the lecture is broadcasted live (LiveStream, Ustream and TwitCam do this) from an amphitheater and you’re standing, I think it’s important to focus on the audience in the room but be aware that there’s a camera around. Your remote audience will know that your main audience is in the room. Moreover, the remote audience is tuning in the lecture/event because they want to see you live but they can’t be there. So the in class lecturing rules would apply here.

Having been through this experience multiple times, I can assure you that it helps to be aware where the camera is and not move too much out of its angle.  It is also important to have a good sound, clear video, good broadband and preferably two cameras to switch between a close-up view (which could be on your or on the screen if you’re using a projector) and a general view (to show you and some part of the audience). But these are more technical guidelines than oratory, speaking guidelines. Professor Andy Miah’s post on setting up an event for YouTube is very good in pointing out what are the areas you should pay attention to when thinking of broadcasting/recording for web.

If your lecture is broadcasted live but you are sitting (so you’re using Skype, TokBox or any other video chat platform), remembering the rules for talking on TV is crucial. In this situation you should try to sit straight and still, you should limit the movement of your hands as well as pay attention to the colors and patterns to wear. However, in trying to look as professional as possible, do not forget your audience. If you are on video via video chat platforms, you’ll most probably be disconnected from your audience so concentrating on the people online is the aim here. I have been through this experience many times as well via the guest lecturers I delivered online throughout the years. The first time I spoke on Skype to class I was nervous, spoke too fast and swiveled in my chair. I realized this when I watched the recording of the video chat and I was sure the students noticed it as well. Since then I have this rule of keeping in mind to whom and through which means I am talking: if it’s while sitting and the audience is remote, I stand still, if it’s in an conference room and I have a projector behind, I move and concentrate on my public in the room. With regards on content, good lecturing as with good public speaking comes with good structure…. Everything else is personal style and as one cannot ask for others to change their music styles, so one cannot expect everyone favor only one kind of lecturing. After all, it’s the experience (and the knowledge) that we’re aiming to make memorable.

Both sessions took place the same day and shared part of the audience.

The morning session about social media in the classroom touched on social media tools to complement, archive and expand the lessons and brought up a lot of questions about integration with the university’s existent infrastructure – such as the university’s internet network, computer labs and Blackboard. While I could answer part of the questions, I have to admit that my usage of Blackboard in the past years was limited and my attention was not centered on integrating the classwork with it but rather circumventing these systems. While I believe that they do have a purpose and they are beneficial and fairly-easy to use, I find them clunky, non-intuitive and visually unappealing. Moreover, while I understand their great role in protecting the students and enabling them to learn in a controlled environment, I believe that new media (including social media, cloud computing and crowdsourcing) present more valuable challenges and lessons to be learned that students can then take into their future jobs (privacy, data protection, ownership rights, authenticity, transparency, respect are just some of them). However, I appreciate the questions asked and I will keep my promise of investigating how the tools I have presented during the training session could be integrated within platforms like Blackboard.

The afternoon session was focused on alternative tools to gather, archive, analyze and visualize qualitative and quantitative data. The tools presented included literature review, annotating and bookmarking sites, qualitative textual analysis tools (Wordle, ManyEyes), quantitative analysis tools, networking and social networking analysis as well as a wide range of social media tools for research, part of them covered in this blog in previous posts. It showed the students that there are many opportunities online that go beyond the licensed products offered by the university or that need to be extensively lobbied for in order to be obtained. It also showed them (I hope) that they can be both creative and exploratory with their research, using new tools to interpret and analyze data obtained using traditional methodologies. The session emphasized also on collaboration and networking among students as well as on having a structured approach to their work where the sections of their dissertations logically and naturally support each other.

Many thanks to the IRO and especially to Heather Lambie for making the training sessions possible and a big thank you to all those who joined me for the day. If you have questions or have more examples that could be added to either of the two presentations, please leave a comment!

On the 16th of November I was invited by Stuart Hepburn, one of Scotland’s top scriptwriters and one of UWS’ Creative and Cultural Industries beloved Lecturer in Performance & Creative Screenwriting, to join his contemporary screen acting students for a day and talk to and with them about using social media to boost their careers. My talk followed an introductory lecture on social media given by my PhD colleague Jennifer Jones, lecture that explained to the students the basic concepts of new and social media and introduced them to blogging via Posterous. My meeting with them was therefore supposed to take them to the next level and challenge them even further.

In order to do so, I have focused on two main aspects:

• that of defining social media through the metaphor of a relationship (where listening needs to preceed talking – hence content creation and making buzz – and where networking needs to support the relationship formed giving the parties involved an opportunities to share their interests but also prove that they listened) and
• that of testing whether the technology influences the story or vice versa.

This enabled me to introduce students to concepts such as RSS and alerts (as options for them to keep up with the news and latest developments of their field) and take them through platforms like Blog Pulse, Who’sTalkin or Google Insights for Search to further explore these developments. This also enabled me to show them some successful and creative uses of social media by artists.

Then, working in teams the students had to create a story using Storify, Jaycut or Animoto. These are three radically different platforms – Storify is Twitter-centered, JayCut is a browser-based movie editor while Animoto is a slideshow alternative enabling almost instant creation of photo-audio slideshows – yet they have extensive sharing and embedding options. The challenge for the students was a multi-layered one and so was supposed to be lesson learned. The students had to create while discovering. They also had to assess whether they will use their own content or integrate and mash-up content already on the internet. Needles to say a discussion about content ownership emerged (both copyright and creative commons). Then they had to think of maximizing visibility thus thinking of their story, its strong points, its appealing points and a method to share their stories on the web. This led the students to discuss about the opportunities of social media but also about the need of assessing them carefully and deciding to use them with a clear purpose in mind. This also made them realize that content can be circulated (recycled) from a platform to another and therefore that social media presences need to be coherent and support each other (content-wise particularly).

I left them with the promise that they’ll continue to explore the platform they’ve discovered during class time and will create or revisit their stories. I also left them enthusiastic at the realization that there is a purpose in what they do online and most of all that there are solutions (rather inexpensive ones – yet sometimes time-consuming) for the technical hiccups of school software.

It’s been a great November morning! I wish the students and budding actors all their best in their careers and hope that Stuart Hepburn will continue to challenge them and future students as well to explore further than the traditional borders of their craft!

The library isn’t the first place one would expect to use social media. On the contrary, some perhaps would ask why would an institutions who is in the business of collecting written/printed words would be interested in anything that is online and most of all that is social and in the cloud? But libraries nowadays are no longer just places for book references. In fact, many libraries that I know provide comprehensive services that range from archival work to digitizing existing and upcoming resources. Moreover, libraries are (or could be) hubs of knowledge and knowledge transfer hence places that need to be linked with both the online and the real world.

Over the past years as a doctoral researcher at the University of the West of Scotland I have seen our library undergo major transformations that affected its way of operating and communicating with its publics (the students, academics and other libraries) as well as its spaces. Perhaps the most astonishing transformation offline is the shift from the unwelcoming dusted reading room on the ground floor in Paisley into a vivid and welcoming cafe and working space where computers work and so does the internet. In the online reals, the UWS Library has been experimenting with social media platforms inviting the entire university to join in the journey.

So when I was invited to hold a 4-hour workshop on social media strategy and policy in mid-November I was humbled, honored and excited. I considered it a great opportunity to engage not only with the library staff and coach them in discovering how to strategically and coherently use social media to connect with the students and researchers, but also though them expand the conversation about social media education, adoption and literacies within higher education institutions.

I have structured the workshop on 4 themes and aimed to make it as engaging as possible, setting challenges and tasks for each segment. We have started with establishing the comfort zones of the participating librarians and exploring what social media means to them and how they see it used/use it in their daily work. This helped me understand their concerns and worries as well as notice the different levels of engagement with social media. We continued by defining and discussing the purposes of each social media platform (with differences between professional and social networking, social bookmarking, sharing and creating) and looking at some innovative uses of social media by other libraries around the world. The list put together by The Search Principle Blog was very good but I also added examples I knew of from my personal experience.

With these examples in mind we then proceeded to analyzing what the UWS Library already does with social media trying to identify audiences and objectives. The library already has a number of presences online including a Facebook Page, a Twitter account and several WordPress blogs such as the official library blog, a science blog and an official publications blog. At the end of the second hour of the workshop the participating librarians produced a list of social media objectives to fit the publics they felt were best to be reached though social media (existing students, researchers and academics but also other librarians) and started a conversation about a differentiated use of their twitter, wordpress and facebook accounts. It also brought up questions about the need of common branding as well as about the necessity of linking these presences with one other for enhanced visibility. The social media audit thus performed and the social media objectives established we proceeded to discussing social media policy where questions about a content creation and uploading timing, content ownership, social media training and response strategies and guidelines were discussed. The participants also produced three different social media policies (two created online using policy generators and one using a guideline post) following to discuss their results and decide on a common a policy by the end of the third our.

The last hour enabled us to wrap up, refine the documents we produced, think about how to increase in real life the visibility of the library’s social media efforts and answered questions raised including about how much time would social media take out of a librarian’s work-load and how much time they should spend. The answers offered by the participants already using social media indicated the existence of a learning curve but also emphasized on the very intuitive nature of social media applications.

For a starter workshop, I hope (and believe) we have manage to lay the foundation of a strategic use of social media by our librarians maintaining them in their comfort zone while also offering them incremental challenges. It would be great if we could continue with other workshops were we would explore in detail each of the social media presences they use and identify ways of improving them and better integrating them within the daily workflow.

I aimed to create a workshop that would put the participants at the center and answer their questions and concerns while giving them plenty of opportunity to experiment and explore within a comfortable environment. And after all, one of the things I wanted to convey, is that like everything and everyone else libraries too need to have clear reasons and objectives of why they embrace social media as they will make their efforts clearer and easier to pursue.

Changes

One of the big changes of this semester’s delivery was that of replacing the academic assignment (a 1-page summary and commentary for 3 of the assigned readings) with a mini-social media audit team project. This change was partly due to the changes in schedule (8 sessions worth of teaching scheduled in 6 meetings) and partly due to the previous students’ positive response to applied exercises. As in previous semesters we used existing companies such as Vanguard Leadership (see analysis), the Library of the University of the West of Scotland (see analysis), Ipadio (see analysis), Encroute Catering (see analysis) and Urban Bar & Brasserie (see analysis). Some of the companies used for the social media audit exercise also provided a guest speaker, giving the class a great opportunity to receive direct feedback to their observations and suggestions. As a final note, students also had to write a reflection blog post about one of the guest lectures, inviting them to revisit, evaluate and put in the context of their studies the lessons learned from practitioners who joined us. All these writings are accessible via the class blogs and wiki:

• Posterous
• WordPress
• Wiki

Evaluation

Judging from the evaluation of the course (to which all 17 students answered) the hands-on exercises were most appreciated even if there were tight deadlines and some technological challenges. The students also reported (in the evaluation but also during class conversations) that they enjoyed the challenge of creating an ad for the class to attract incoming students. You can see some of the videos here, here and here.

I am humbled and honored to have received such positive evaluation. More than 50% of the class assessed the course organization and its effectiveness as very good with elements such as my use of technology and examples receiving an excellent vote from more than 60% of the students. The report below shows the entire evaluation.

Lessons learned

Similar to the previous semesters most of our guest lecturers joined the class virtually via Skype or TokBox. The interaction between the students and the guests was very good (provided that the technology didn’t fail us and we didn’t lose connection). When our call with John Coster and  Tina Barton was severely interrupted (we couldn’t even establish an audio connection) the students turned to Twitter to share their questions. They received the answer to their questions in a short video response that Tina recorded on my TokBox. This proves again, that there is great potential for integrating technology in the classroom as a means of enriching the class experience and expanding the students’ horizons. This also shows that technology can be used to complement the class delivery and strategy.

However, a lesson learned this semester includes that of capturing and maintaining the students’ attention during the hours of the course. Encouraging students to use Twitter as a back channel, to check out online resources, add photos to the class Flickr group and complete small research and analysis exercises during class time ensured that their attention was maintained most of the time. As usual, there is always place for improvement.

All these being said, I have enjoyed this semester and I am looking forward to my next group and my next class.

It’s official! I am one of the 15 Advisory Board members of SMCEDU, an education initiative of Social Media Club (founded by Chris Heuer) focused on improving the quality of Social Media education in Universities and empowering its community of Social Media professors.

I have been following the Social Media Education chats ever since their first Twitter edition last November. During these 1-hour sessions, educators, students and practitioners interested in social media education have asked and answered many challenging questions ranging from what a social media curriculum should entail to who (as in departments and people) should teach such courses. It is in these chats that I have received peer feedback for the Katho New Media course as well as found inspiring examples and case studies to use in my guest lectures, training sessions and workshops.

Today, SMCEDU has designated its board members and I am honored to be one of them.

“With the addition of the advisory board, we will begin our work in earnest to produce a set of programs and materials that will support social media professors, universities and most of all students around the world,” said Yong C. Lee, SMCEDU Director.

Among the tasks my colleagues and I will have will be determining the priorities for SMCEDU, assisting in securing grants for its programs and identifying key insights for success.

Personally, I plan to actively advocate for more social media research that includes experimentation of free or open source software and for a wider adoption of social media in the general communication strategies of universities and academics.

A list of the board members and where you can find them is below. The official announcement is here.

• Alex de Carvalho
  • Twitter: http://twitter.com/alexdc
  • LinkedIn: http://linkedin.com/in/alexdc
  • Facebook: http://facebook.com/alexdc
  • Blog or website: http://alexdc.org
• Terry Eberhart
  • Twitter: http://twitter.com/Digin4ed | http://twitter.com/taeberhart | http://twitter.com/mccprofe
  • LinkedIn: http://www.linkedin.com/in/terryeberhart
  • Facebook: http://www.facebook.com/terry.eberhart
  • Blog or website: http://web.monroecc.edu/teberhart | http://digin4ed.bridgecrew.net |http://teeconomics.bridgecrew.net/ | http://profe.bridgecrew.net
• Dawn Gilpin
  • Twitter: http://twitter.com/drgilpin
  • LinkedIn: http://linkedin.com/in/dawngilpin
  • Facebook: http://facebook.com/dawn.gilpin
  • Blog or website: http://www.dawngilpin.net
• Vicky Hennegan
  • Twitter: http://twitter.com/@vickyhennegan | http://twitter.com/eeUS
  • LinkedIn: http://www.linkedin.com/in/vickyhennegan
  • Facebook: http://www.facebook.com/VickyHennegan
  • Blog or website: http://remarkableparents.com
• Paul Hyland
  • Twitter: http://twitter.com/paulhyland
  • LinkedIn: http://www.linkedin.com/in/paulhyland
  • Facebook: http://www.facebook.com/paulhyland
  • Blog or website: http://www.edweek.org/
• Mickey Mantas
  • Twitter: http://twitter.com/mickeymantas
  • Facebook: http://www.facebook.com/michelinamantas
• John McArthur
  • Twitter: http://twitter.com/jamcarthur
  • Facebook: http://facebook.com/jamcarthur
  • Blog or website: http://jamcarthur.com
• Marcus Messner
  • Twitter: http://twitter.com/@marcusmessner | http://twitter.com/@socialprofessor
  • LinkedIn: http://www.linkedin.com/in/mmessner
  • Facebook: http://www.facebook.com/group.php?gid=142595775776287&ref=ts
  • Blog or website: http://www.marcusmessner.com
• Andrea Genevieve Michnik
  • Twitter: http://twitter.com/AndreaGenevieve
  • LinkedIn: http://www.linkedin.com/in/andreagenevievemichnik
  • Facebook: http://www.facebook.com/andreagenevievemichnik
  • Blog or website: http://www.andreagenevieve.com
• Andi Narvaez
  • Twitter: http://twitter.com/andinarvaez
  • LinkedIn: http://www.linkedin.com/in/andinarvaez
  • Facebook: http://facebook.com/andinarvaez
  • Blog or website: http://andinarvaez.com
• Dana Nelson
  • Twitter: http://twitter.com/DanaMNelson
  • LinkedIn: http://www.linkedin.com/in/DanaMNelson
  • Facebook: http://www.facebook.com/DanaInc | http://www.facebook.com/DanaMNelson
  • Blog or website: http://www.DanaMNelson.wordpress.com
• Andy Petroski
  • Twitter: http://twitter.com/@apetroski
  • LinkedIn: http://www.linkedin.com/pub/andy-petroski/0/176/315/
  • Facebook: http://www.facebook.com/profile.php?id=755429667
  • Blog or website: http://learnev.blogspot.com/
• Steve Radick
  • Twitter: http://www.twitter.com/sradick
  • LinkedIn: http://www.linkedin.com/in/sradick
  • Blog or website: http://www.steveradick.com
• Corinne Weisgerber
  • Twitter: http://twitter.com/corinnew
  • LinkedIn: http://www.linkedin.com/in/corinnew
  • Facebook: http://www.new.facebook.com/corinne.weisgerber
  • Blog or website: http://socialmediaprclass.blogspot.com/

For some time now, I am doing my best to participate in a Twitter chat dedicated to social media measurement, #smmeasure, as a means of exploring what practitioners do and of discovering new tools. Last Thursday (November 4) the discussion who brought together marketing/communications practitioners, bloggers and representatives of social media measurement companies have focused on influence and influencers – identifying them, measuring their influence/reach, engaging them. So besides the need of defining these notion (which Andreana Drencheva, or @addy_dren attempted to do in her post) a necessity to reveal and discuss tools used for research emerged. In this post I am going to list the Twitter tools I used so far and group them into measurement objectives/categories.

Activity evolution

TwentyFeet is a rather new platform dedicated to aggregating statistics related to Twitter and Facebook accounts. Once authorized, the platform collects and produces visualizations based on activity metrics: followers, lists, mentions, RTs, favorites, links and more. What’s interesting is the platform’s interpretation of data into reputation (followers and lists), influence (mentions, RTs), conversation indicators (Tweets, RTs, @replies). While I believe it is true that the number of followers could indicate an increase in reputation as well as an increase in the number of mentions could point out to an increase in an account’s influence, without any context to the data such assumptions are very difficult to prove. Take for instance the existence of automated following programs or the involvement of people in weekly chats. In these cases the number of followers of one account can be artificially increased. Similarly, with the participation in a weekly chat there is a higher potential for an increase in mentions, however this does not necessarily equal with an increase in influence.

TwitterAnalyzer provides a variety of activity evolution metrics including user activity (number of tweets, chats, subjects, hashtags, links) and follower metrics (online followers, growth rate, density map, RTing accounts) and user interaction (mentions). This last category is a little bit unclear as it provides statistics for both mention updates (as made in public tweets) and social mentions (as made in chats). If you consider that most tweets and chats are public, than this differentiation doesn’t mean much unless it makes reference to automates posts (as in generated for instance by paper.li and other automated updates) versus @ mentions. One advantage of TwitterAnalyzer is that integrates qualitative data (as in tweet excerpts) in the same window giving thus context to the visualized quantitative data. The platform also provides metrics for reach (daily exposure rate) and popularity (could be understood as influence; also shown on a daily basis) but is is unclear what algorithm is used to calculate them.

TweetEffect visualizes the fluctuations of followers for designated account. It uses data from the last two weeks of a given account and visualizes it in two ways – first as a simple graph, then a table that combines qualitative and quantitative data. The table shows an account’s activity tweet by tweet and highlights its presumed influence on gaining or losing followers. However, the platform doesn’t yet take into account that the loss of followers could be influenced by the account holder’s decision to block access of other users.

TweetStats visualized Twitter usage patterns for designated accounts including graphs for tweeting timeline, daily tweeting density, aggregate daily and hourly tweets, platforms used, mentions made by the account and RTs made by the account. This is a good tool for behavioral and usage patterns observations and can, if used consistently, highlight technology adoption and working patterns. Used in combination with TwitterAnalyzer that shows the subject about which an account tweets, TweetStats could show when people tweet and help thus communicators find out both the what and the when that makes an influencer tick.

Xefer is an alternative to TweetStats. Their graphs confirm each other meaning that they most probably use similar algorithms and similar data.

TwitterCounter track the activity of an account showing the fluctuations in numbers of followers, followed accounts and tweets. Premium services provide metrics for mentions and RTs, but with the existence of platforms like TwitterAnalyzer and Twitter’s own metrics one could wonder whether they are indeed providing a unique service.

Rank

TwitterGrader is measuring the power, reach and authority of a twitter account. Its algorithm includes a number of factors such as number of followers, power of followers, updates, update recency, follower/followers ratio and engagement. According to this post, the score is calculated “based on the factors mentioned and then used to compare a user against all other users that also have a score.  The grade is calculated as the approximate percentage of other users that have an equal or lower score.  So, a Twitter Grade of 80 means that about 80% of the other users got a lower score.  At the time this article is being written, over 2.1 million users have been graded”. Using a ranking application can be useful when aiming to find out where accounts that one follows or is followed by exist within the Twitter sphere. However, without context – and by context here I mean observing the tweeting behavior and topics covered by one account – rankings are just numbers.

TweetGrade grades an account’s activity based on a quantitative assessment of the account’s “reach and influence in the Twitter community. Based on the account’s interactions with others, the frequency and content of updates, and the account’s overall contributions to Twitter” the platform assigns a simple letter grade that ranges from an ‘F’ to an ‘A+. This could be a good platform for beginners in search for a bit of Twitter fun.

RetweetRank provides a score for the number of times a user has been retweeted by others recently (though it doesn’t say how recent) and it doesn’t say to whom the percentile is reported to. For my score (98.67% on November 6) I assume that the interpretation of the result would indicate than I am RTed more than 98% of the other assessed Twitter users… Promising but hard to believe. Such metrics depend on a wide range of variables including size of network, relevance of message and even time of tweeting.

Twitaholic displays a number that is supposed to be a user’s rank within Twitter. According to the platform, they scan twitter a few times a day to determine who’s the biggest twit but they do not say how do they calculate this: whether is a ratio of followers/followed or a ratio between followers/followed/tweets. Moreover, while the number given might be encouraging or quite the opposite, the rank is calculated in relation to entire Twitter user base, making the results quite useless if looking to rank as a measure of popularity/visibility. This could be much better if geo-location information would be added.

Tweerank presents itself as an ”engagement score” which is assigned to any Twitter user, which has to do more with conversation and reach in my view. According to the platform the score takes into account a multitude of parameters includingthe number of followers, the number of followed accounts, the number of tweets, RTs and mentions. Since there no information to what dataset the rank is reported, I assume it calculates the percentile looking at the entire Twitter-sphere.

Ranks are also calculated by CrowdEye but there is little detail on how that “cool math” really works.

Reach

TweetReach provides a comperehensive set of metrics about a search term or user impact on Twitter and complements the quantitative data with excerpts of qualitative inputs. An explanation of how the scores are calculated is available here. Calculating reach is useful when aiming to assess the number of exposures a message/account could gain as facilitated by its network.

As mentioned earlier, TwitterAnalyzer provides reach metrics as well but offers less details about how it obtains its data for the visualizations it produces.

Influence

TwitAlyzer provides a score for impact and an assessment for the type of influencer the scored account would be. The impact score is the result of a number of factors (number of followers, unique references and citations, unique RTs, tweeting frequency) but the formula is not disclosed. The score displayed as percentile is reported to the total of Twitter accounts the platform is tracking. This posits me in the superior 77+%, meaning that my impact score is higher than 77% percent of the 0 active Twitter accounts they are tracking. The platform also provides a set of definitions for the terms they use, a welcomed feature when attempting to understand the terminology. The promising feature of TwitAlyzer is it’s geo-location. I am still unsure how they integrate their impact score with the city/area of the Twitter account but should this be possible, those statistics would help researchers and communicators find relevant tweeple.

Twinfluence “is a simple tool for measuring the combined influence of twitterers and their followers, with a few social network statistics thrown in as bonus”. I have to applaud the platform for having the most detailed information about how and where the data is retrieved, the algorithm used and the definitions employed. Twinfluence goes beyond calculating a rank of a given account, by providing metrics about how tight its network is (centrality), the accumulated influence (velocity) and the social capital of an account (the average first-order network). These metrics are highly important as one’s network could be tightly or loosely knit making for instance the information shared to spread at different speeds. Twinfluence could be paired up with FlowingData’s MentionMap that visualizes the network of a given twitter account.

Klout scores range from 0-100 and measure the overall online influence of a Twitter account. By overall I assume they mean twitter overall rather than internet overall. According to the platform, the score is “a factor of over 35 variables broken into three categories: True Reach, Amplification Score and Network Score” where “True Reach is the size of the account’s engaged audience and is based on the followers and friends who actively listen and react to your messages. Amplification Score is the likelihood that an account’s messages will generate actions (retweets, @messages, likes and comments) and is on a scale of 1 to 100. Network score indicates how influential an account’s engaged audience is, also on a scale of 1 to 100. The Klout score is highly correlated to clicks, comments and retweets.” Perhaps the most useful of Klout’s features is its influence matrix interpretation of the score that makes it appealing even for social media beginners.

Volume/Trends

Trendistic enables comparative searches of terms within Twitter. It is great for contextual analysis since the visual data (that can span from 24 hours to 30 days) is supported by the inclusion of qualitative data (the tweets that were used to generate the visuals). It is also a great tool for analyzing words in context as well as observing emergence of trends and patterns of communication. Correlated with data from outside of Twitter, such as media reports or personal data Trendistic could help summarize the communication ups and downs of an event as translated by the attention/mentions received.

TweetVolume enables comparisons only between 3 search terms. Since last time I visited the application, considerable improvements were made including clarifying where the information is gathered and how the graphs are generated (calculated): “the TweetVolume results are an estimated count of the number of times a word or phrase has appeared in a Twitter post. We generate the estimates by utilizing resources from Google’s search engine and Twitter’s open API to capture and compare data points.” The platform now also displays tweets included in the graph generation.

The alternative to TweetVolume is Monitter that displays tweets related to 3 search terms. While the platform doesn’t visualize the data nor calculates volumes, it does however filter it based on geo-location data.

Twopular not only tracks emerging trends but enables one to compare up to 10 of them. This is useful when comparing alternative hashtags as well as tracking the variations in volume of mentions of words, hashtags, brands in time.

Twitscoop that also generates comparative graphs based on volume of mentions of notions and hashtags. Like Trendistic it includes the tweets as well.

Other trends search/display platforms but without the visualization are Topsy and TweetMeme.

Sentiment

Twittratr‘s screen includes a sentiment meter and 3 columns dedicated to positive, negative and neutral sentiment related to a searched term. The sentiment is calculated using an existing list of negative and positive words. This could be good but is not ideal as it lacks context. Some words for instance are negative or positive by default (take sadness and happiness for instance) but they can change meaning depending on the words surrounding them (as in abolish racism or promote racism). For this automated list to work it would not only need constant updating but also an addition of combinations of words that can be positive or negative.  While the number of total tweets is reported making it easy to understand the sample, it is still unclear what the time span of that sample is. The displayed tweets do include links to Twitter but this complicates rather than eases the work.  A similar modus operandi is displayed by TweetFeel and TweetSentiments with the latter being more complex. To its display of negative, positive and neutral sentiment percentages and color coding the tweets, TweetSentiments also includes an assessment of the education, flamboyance, slang, gender and age which could be particularly relevant when assessing accounts rather than events.

CrowdEye mentioned earlier for its ranking metrics, is a promising sentiment analysis platform. It provides volume of searches for as much as 14 days, sentiments split into positive and negative boost and geo-location filtering options. To these the tweets displayed include the CrowdEye rank of the user enabling one to potentially assess the impact and reach of the tweet. Reading and interpreting the data would be facilitated if platform adopted the color coding system of Twitrattr and TwitterSentiments.

Other Twitter sentiment analysis platforms worth noting are Twendz and Chatterscope, both developed by Public Relations agencies, Waggener Edstrom and Lewis PR respectively. Both platforms (the free versions) provide similar data but visualize it differently. Moreover, Twendz continues to update its content unlike Chatterscope that displays its metrics based on the last 1000 tweets found. Additionally, Twendz also includes subtopics and a wordcloud enabling further exploration of related content.

Archive

TwapperKeeper allows one to create an archive for a hashtag, keyword or person making access to Twitter data even after the search API expired easy. If set up in advance the archive can be an invaluable source of information that will help track evolution, sentiment, volume, behavior, network connections after the event is over. Archives will also permit comparisons, something desirable when attempting to look at research precedents or to investigate previous practices. An alternative for TwapperKeeper is the Twitter Tag Downloader, but unlike TwapperKeeper it does have a limit 5000 tweets.

The Archivist also archives twitter data but it has the added value of providing some visual interpretation by default. This makes the analysis process even easier than with TwapperKeeper without taking out the flexibility of investigating the data outside the given patterns since it has options for viewing and downloading the data. Moreover, the Archivist has also a desktop application which would enable working with other datasets as well.

Other ways of archiving Twitter include back-up options reviewed by ReadWriteWeb. Of some relevance might be Twistory and TwInbox, both downloading data to calendars and Outlook Express respectively. Personally, I see these two options as good for simple back-up but difficult for more in-depth data analysis.

A final word

All the tools I have mentioned here are free or operate on a fremium model. Before using them it is highly recommended that you have some objectives that you seek to evaluate. Then, in assessing those objective do use more than one tool in order to verify results.

What tools do you know? What tools have you used?

During the 12th and 15th of October, the University of Hamburg was the host of one of the biggest and most renowned communication research and education conferences: ECREA.

The conference featured 17 sections and welcomed more than a thousand delegates from Europe and beyond. While many of the papers presented sounded interesting, the incredibly busy schedule spread over 10 rooms, several parallel sessions and an entire campus made it difficult to attend them all. Moreover, the schedule was extended unlike any conference I have ever attended up to 8PM in the evening giving not only little visibility to the evening presenters but also putting them in the difficult position of maintaining interest and attention after more than 10-12 hours of conference time.

However, ECREA was the conference were I had the opportunity to be present with two papers: one in collaboration with my colleague Jennifer Jones on our work around online narratives at the Olympics and one premiere presentation of the full results of my dissertation. While the hours were late and the time scarce, I took the challenge of presenting my entire doctoral work in 15 minutes as a baptism of fire and preparation for my future defense.

The video (livestreamed during the conference and recorded thanks to Jennifer’s help) is below. While sharing some of the results, I stressed on the methodological innovation that the thesis brings. This, coupled with Jennifer’s presentation, led to calls for future workshops on social media methods and research from conference participants.