Harith Alani talked about using semantics to solve problems around evaluating the success of social media use in business. The SIOC ontology is widely used to describe online community information. It's not as simple as measuring someone's engagement with a brand's online presence - people are 'likeaholics' on Facebook, so you have to look at someone's whole behaviour profile to judge whether their like means anything or not. It's no good just aggregating your data and spewing out numbers - you have to browse the data and try to understand where it came from.
He mentioned how little work has been done in classifying community types. Most of the work that has been done seems to be with social networks internal to an organisation. A bottom-up approach to community analysis can handle emergent behaviours and cope with role changes over time. Looking at behaviour categories and roles can help an organisation to decide who to concentrate on supporting and how in order to sustain the community. The results they have seen so far suggest that a stable mix of the different types of behaviours are needed to increase activities in forums - but they don't know what causes what. They're reaching a point where they can use their behaviour analysis to guess what's going to happen to a community: how long it will last, how fast it will grow, how many replies a certain type of post is likely to get, etc.
Next they want to be able to classify community types, and be able to look at activities within a community over a period of time and automatically discover what kind of community it is; it might be something different than what it was set up for.
They created an alternative Maslow's Hierarchy of Needs to correspond with activities seen on forums, and found that most people are happy to stay at the lower levels of the hierarchy. For example, join a community, lurk for a bit, ask one question and leave. Not everyone wants or needs to be a power user.
Papers are being written that find patterns in individual datasets for a particular community in a particular context. Harith and his team are getting tired of this; they want to generalise across communities. So they took seven datasets and looked at how the analysis features differed as well as comparing the results across community types, randomness (vs. topicality) of datasets, and compared similar experiments.
Upcoming work includes the Reel Lives project, in which UoE is involved. They're taking media fragments - photos, videos, audio clips, text recorded as audio - and creating automated compilations to tell a story.
Another is social methods to change energy consumption behaviour. LiSC in Lincoln did something in this area back in the day.. an app that posted that you were listening to an embarrassing song on your facebook feed if you left your lights on.
Notes from Harith's talk are here.
From Tommaso Di Noia's talk, I learnt that recommender systems have a lot of maths behind them, especially for evaluating things, and reinforced something I already knew: I don't maths good enough to be taken seriously by most of the Informatics world. I think I understand the principles behind the maths, but when something is descried in just maths, I have no idea what it relates to. I'll work on this.
Real world recommender systems use a variety of approaches, including collaborative (based on similar users' profiles); knowledge-based (domain knowledge, no user history); item-based (similarities between items); content-based (combination of item descriptions and profile of user interests). Linked Open Data is used to mitigate a lack of information about entities, and helps with recommending across multiple domains. You do have to filter the LD you use before feeding it to your recommender system though, to avoid noise. Notes here.
Tommaso's talk was followed up by a hands-on session, where we got to poke about with some of the tools he mentioned, including FRED (transforms natural language to RDF/OWL); Tipalo (gets entity types from natural language text); and using DBpedia to feed a recommender system.
Then we worked on our mini-projects for the afternoon. We made some progress towards breaking down the concept of serendipity and working out what properties we might need to represent as linked data, and how we could observer a user and work out if/when/how they were having serendipitous experiences without intruding too much.
In the evening we took a coach to 'nearby' historical town Segovia. Apparently an extremely motion-sickness-inducing two and a half hour coach journey around twisty mountain paths is 'nearby'. Fortunately I was distracted from this horrible journey by a conversation with Lynda Hardman, which I wish I had recorded. Lynda challenged various aspects of my PhD until I could explain/justify them reasonably, including:
She also recommended a number of resources, including theses of her recent former students to help me with a structure for my own, and advice on maintaining a healthy balance between thinking and doing.
Plus she used to live in Edinburgh, more or less across the road from where I live now. Cool. Thanks Lynda! You haven't heard the last of me :)
#travel
Once we got to Segovia, we had a guided tour of the ancient Roman architecture, interesting building façades and local legends. It was a very good tour, but too hot to really focus. Then they took us to a restaurant for a local speciality. I was all set to write a whole individual blog post surveying the barbaric nature of human beings, but I didn't do it straight away and now the passion has faded slightly, so I'll leave it at a paragraph. Some people watched the local 'ceremony' out of morbid curiosity I imagine, but it was the fact that so many people took so much pleasure in the idea of violently hacking up bodies of three-week-old piglets that really bothered me. Fortunately the surging standing crowd allowed me (and only one other) to inconspicuously sit it out. The veggie option was tasty, but it was difficult to really enjoy the rest of the evening whilst wondering vaguely about the states of minds of most of the people I was sharing a table with.
He mentioned how little work has been done in classifying community types. Most of the work that has been done seems to be with social networks internal to an organisation. A bottom-up approach to community analysis can handle emergent behaviours and cope with role changes over time. Looking at behaviour categories and roles can help an organisation to decide who to concentrate on supporting and how in order to sustain the community. The results they have seen so far suggest that a stable mix of the different types of behaviours are needed to increase activities in forums - but they don't know what causes what. They're reaching a point where they can use their behaviour analysis to guess what's going to happen to a community: how long it will last, how fast it will grow, how many replies a certain type of post is likely to get, etc.
Next they want to be able to classify community types, and be able to look at activities within a community over a period of time and automatically discover what kind of community it is; it might be something different than what it was set up for.
They created an alternative Maslow's Hierarchy of Needs to correspond with activities seen on forums, and found that most people are happy to stay at the lower levels of the hierarchy. For example, join a community, lurk for a bit, ask one question and leave. Not everyone wants or needs to be a power user.
Papers are being written that find patterns in individual datasets for a particular community in a particular context. Harith and his team are getting tired of this; they want to generalise across communities. So they took seven datasets and looked at how the analysis features differed as well as comparing the results across community types, randomness (vs. topicality) of datasets, and compared similar experiments.
Upcoming work includes the Reel Lives project, in which UoE is involved. They're taking media fragments - photos, videos, audio clips, text recorded as audio - and creating automated compilations to tell a story.
Another is social methods to change energy consumption behaviour. LiSC in Lincoln did something in this area back in the day.. an app that posted that you were listening to an embarrassing song on your facebook feed if you left your lights on.
Notes from Harith's talk are here.
From Tommaso Di Noia's talk, I learnt that recommender systems have a lot of maths behind them, especially for evaluating things, and reinforced something I already knew: I don't maths good enough to be taken seriously by most of the Informatics world. I think I understand the principles behind the maths, but when something is descried in just maths, I have no idea what it relates to. I'll work on this.
Real world recommender systems use a variety of approaches, including collaborative (based on similar users' profiles); knowledge-based (domain knowledge, no user history); item-based (similarities between items); content-based (combination of item descriptions and profile of user interests). Linked Open Data is used to mitigate a lack of information about entities, and helps with recommending across multiple domains. You do have to filter the LD you use before feeding it to your recommender system though, to avoid noise. Notes here.
Tommaso's talk was followed up by a hands-on session, where we got to poke about with some of the tools he mentioned, including FRED (transforms natural language to RDF/OWL); Tipalo (gets entity types from natural language text); and using DBpedia to feed a recommender system.
Then we worked on our mini-projects for the afternoon. We made some progress towards breaking down the concept of serendipity and working out what properties we might need to represent as linked data, and how we could observer a user and work out if/when/how they were having serendipitous experiences without intruding too much.
In the evening we took a coach to 'nearby' historical town Segovia. Apparently an extremely motion-sickness-inducing two and a half hour coach journey around twisty mountain paths is 'nearby'. Fortunately I was distracted from this horrible journey by a conversation with Lynda Hardman, which I wish I had recorded. Lynda challenged various aspects of my PhD until I could explain/justify them reasonably, including:
- Why digital creatives? (I'm used to that one now).
- What is the outcome?
- Why Semantic Web for this?
She also recommended a number of resources, including theses of her recent former students to help me with a structure for my own, and advice on maintaining a healthy balance between thinking and doing.
Plus she used to live in Edinburgh, more or less across the road from where I live now. Cool. Thanks Lynda! You haven't heard the last of me :)
#travel
Once we got to Segovia, we had a guided tour of the ancient Roman architecture, interesting building façades and local legends. It was a very good tour, but too hot to really focus. Then they took us to a restaurant for a local speciality. I was all set to write a whole individual blog post surveying the barbaric nature of human beings, but I didn't do it straight away and now the passion has faded slightly, so I'll leave it at a paragraph. Some people watched the local 'ceremony' out of morbid curiosity I imagine, but it was the fact that so many people took so much pleasure in the idea of violently hacking up bodies of three-week-old piglets that really bothered me. Fortunately the surging standing crowd allowed me (and only one other) to inconspicuously sit it out. The veggie option was tasty, but it was difficult to really enjoy the rest of the evening whilst wondering vaguely about the states of minds of most of the people I was sharing a table with.
