Investigating the Matera-Lote4 twitter community

(pictures album here)


Figure 1 - A view at heart of the twitter nebula

When organizing an event, we can reach out using Twitter, but what is at the heart of these Twitter discussions and on what’s the (new?) definition of a community in the twitter space?

We try to describe in this document a study of an analysis of Twitter, with a top-to-bottom approach: from the top-level general questions around the idea of a community we drill down to the bottom-level data gathering, analysis, and finish with visualization of the data.

Through this document, discussions with experts has redefined the notion of community along three different axes and analyse our twitter communities around these axes.

The document presents first the highest level questions we have wondered on this Twitter community, then the data gathered, followed by the ground baselines on which we’ve looked at communities, and finally the analysis.

The question

So we’ve gathered many experts, our Masters of Networks (Community Managers Lee-Sean Huang, Noemi Salantiu, Laura Manconi, Rosa Strube and Collective Intelligence Researchers Marta Arniani, Yannis Treffot, Benoit Gregoire and Network Scientist Benjamin Renoust).

The first idea was to find the many questions that we can build around the Lote4 twitter community. The main idea was to identify first if and how a Twitter “conversation” around a hashtag can form a community?

One of our assumptions is that we can find that there are lots of isolated components in a Twitter hashtag stream, with people not really calling out to each other, whereas “tight” online community gives rise to a giant component that most of the nodes are connected to. Can we confirm that from a data perspective?

Another assumption is that people could easily form subgroups investigating specifics, and how we can find traces of, or understand the content gathering these subgroups?

The data & context

To that purpose, we have analyzed twitter data made available during the MoN3 event.

But before talking about the data itself, we may mention some contextual information on how this data has been captured.

This data represents Tweets collected between 18/10/2014 and 23/02/2015, this data has been gathered from the search query “#lote4 OR edgeryders OR unmonastery OR Matera” by @piersoft using TAGSExplorer.

Lote4 stands for Living On The Edge, a conference organised by the Edgeryders global community which took place in Matera (Italy) between the 23rd and the 26th of October 2014. The unMonastery was an artist and hacker residency program that Edgeryders ran in Matera during most of 2014; the Lote4 conference happened in the unMonastery building and with the help of the unMonastery events. The search string was expected to catch all tweets around the event taking place in Matera and their follow up over subsequent months.

Organizing the event, we expect authors such as Edgeryders and Matera2019 to be sort of moderators of the event, and to engage other twitter users from their own networks in driving conversations among the different participants.

The collection is composed of about 20k Tweets written by 7000 people involving another 1000 additional people (via mentions or RTs).

The data has information on who sends a Tweet, eventually whom the Tweet is sent to, who is mentioned in the Tweet, the date and which hashtag has been used.

The notion of community

We need to step back a bit here and question ourselves on what makes a community a community. Social network scientists such as myself have preconceived established notions of what makes a community in terms of data analysis, but they all end up being empirical and somewhat fitting well in the boots of data analysis. Two main definitions might be recalled, the first one would come from Manski, for whom the group effect builds from gathering people alike (translated in data processing this would mean similarity of attributes). The second definition is used as a support to compute the Newman’s modularity, states that a community has much more relationships between members within itself than with other members from outside the community. Discussions led by our panel of experts has pushed even further the different definitions of the notion of community.

We’ve tried to bring different perspectives on the notion of community, by asking these questions  “what does it mean to be a community? what does it mean to belong to a community? what does it mean to look at a community?”.

We gathered many answers which faceted a bit the notion of “community”, into three main categories, and we’ve also discussed some other interesting characteristics of communities.




Sense of belonging/endeavor

inner sense of belonging

sharing of interest

some commonalities

publish on similar twitter hashtag

gather around specific goal

share content

People talking to one another

exchange in the community (both ways)

follow the same people / sign petition

actually meet / community of practice

people who do more than what they need to/have to


can be negative engagement

Other characteristics

groups = set of people

transversal to existing organization

classes of communities hierarchical

over time can fuse or divide

somebody who’s not in the community (the rest of the world)

Table 1 - Summary of different characteristics a community can have

Our experts have extracted 3 levels that define a community from this point:

  • the awareness
  • the exchanges/discussions
  • the action/actual engagement

Community in Twitter

The next step is to reconnect these notions of a community with evidences we can find in the data. In other words, what does “awareness”, “exchanges” and “engagement” mean in the context of Twitter publications?

In the world of Twitter, someone’s awareness can be measured by the semantics these people use, i.e. the hashtags they use in publishing so we can measure how often these keywords appear, the number of other users who relate to the same semantics, and the presence of our users and their posts on different platforms.

Sharing and exchanging between users is the basic purpose of such a micro blogging platform. This type of interaction can be represented in the world of Twitter by mentioning somebody or replying to somebody: it never means that an actual engaging conversation is going on, but it initiates potential collaboration.

Other measures can be of interest in tracing the interactions within a community (well, when the community is defined already). The number of connections (following, followers) of a user, their amount and frequency of posting, and the impact of the posts: how do others in the community endorse the posts? does it generate spin offs? all within the community, and out of the community? How to measure this impact? of each post? of each individual?

The network of practice in the material engages people in meeting and actually doing things together, working toward a common goal. We could find traces of physical presence, at events for example, of people from the geocoding, the hashtags they use, when related to an event, or via cross platforms activities such as FourSquare. Unfortunately these indications are not really reliable when confined to the sole Twitter information. Engagement on Twitter can take different shapes, it can mean reciprocal interactions, with the production of content and maybe some spin off actions. One reliable action on Twitter is the construction of actual conversation between people, meaning people replying to one another, reciprocally not only broadcasting information, or commenting on shared interest but real conversations.

Among the other characteristics of a community discussed, the most interesting would probably be the influence of time on the group evolution (fusing/dividing), but we’ll keep these aspects for a different analysis.

Notice that we have yet taken the “RT” or “retweet” relationship out of the picture as it is a versatile information. This action is the easiest and most represented action of the Twitter universe. The act of retweeting can bear two different meanings. It first helps showing your interest, people retweeting similar posts are show similar awareness, but it can be either positive or negative engagement. The number of retweets can weighs actually the tweets, because when a tweet has been retweeted a lot can be considered as “impactful” or just popular.

The analysis

General analysis of the Twitter data

So the dataset is composed of ~20,000 tweets gathered from the query string “#lote4 OR edgeryders OR unmonastery OR Matera” between 18/10/2014 and 23/02/2015, published by ~7,000 different authors, involving 8,000 people including mentions and replies, and referring to over 6000 hashtags.

Here is the production of tweets, we can clearly see a few activity peaks around the event, first during the period of preparation, before the actual event, then during the event.

The tweets also peak around half November, and more activity can noticed between the end of December and the beginning of January.

Figure 2 - The production of tweets over the period of capture

Among the 7000 authors, only 42 have produced over 50 tweets in the period of time, and 18 users have only retweeted information, about 850 more have published over 5 tweets in this period of time (and actually 200 are only retweeting information).

Figure 3- Occurrence of hashtags over time. Of course, #matera is the most occurrent over time in the dataset. Spikes correspond partly to #btwic2014 & #lote4 (before Oct. 26) #saleritana, #basilicata #matera2019 #mendicino (from Nov. 02) #labuonascuola #vivoazurro #under21 #matera2019 (around Nov. 16) #capodanno #genova #neve #matera2019… (end of Dec, early Jan.)…

Figure 4 - Distribution of users (nodes) per number of tweets produced

So retweets generate a background noise and we’ll keep them apart for a secondary analysis.

When we remove the RTs, we can consider a total of 4500 twitterers replying and mentioning each other.

The network they compose is very disconnected, and half users captured here discuss in small groups of at most 10 people, producing each very little tweets. However the other half users (around 2100) are involved in a gigantic twitter conversation.

Figure 5 - The main connected component of twitterers, each node is a twitterer, each link a reply or a mention between two twitterers

2100 twitterers discussing about Matera, Lote4 and many other things. The size of a node is the number of tweets produced in the collection, the color of a node is its centrality. An edge means a direct reply and/or mention between two users.

Communities in Twitter: drilling down to the heart of the community

Following the previously defined criteria, we’ve tried to define how is this community composed around the twitter hashtags, mentions and replies. Because we’re looking for the strongest evidences of “communal” behaviors between twitter users, we advanced quick towards traces of engagement between users. We have therefore considered first the “reply” relationship between users, and we’ve drilled down to only 500 of the 2100 users who are actually replying to each other in a big conversation (1600 are involved in small conversations).

Figure 6 - 500 users replying to each other. We can notice the arborescent structure of some nodes.

Now, looking for the strongest ties, we want to subset even further these conversations to identify actual traces of reciprocal conversations, i.e. replies over replies.

Only 21 people are actually taking part of conversations involving more than just a triplet of users, and this group is actually divided into 2 disconnected subgroups.

Figure 7 - The 21 core individuals involved in reciprocal conversations.

One subgroup is focused on Edgeryders, producing altogether 490 tweets, and the other group focused on travel companies and tourism, producing 171 tweets.

A quick search on how it distributes over time tells us that those two conversations happen at two different timings, the Edgeryders community happens mostly in the early period and the second group is focused later.

Figure 8 - Distribution of the tweets related to Edgeryders among time (selection in pink).

Communities in Twitter: going back to notion of community

Now, we may wonder how the two rather central communities have been brought close together, and how do they interact together? We can step back and look at how the different twitterers do at mentioning each other.

1500 people actually mention each other in a connected way. Among these 1500 people, only 100 form a core of reciprocal mentions between each other’s, i.e. people acknowledging each other.

Figure 9 - Two consecutive mesmerizing zooms on 1500 people mentioning one another in an intricate conversation.

Now it is interesting to see how the core two communities we have previously identified collaborate with one another. We can observe that the two sub-communities do not acknowledge reciprocally each other.

Figure 10 - Highlight (in pink) of the two communities in the connected components of the “Mention” network

However we can also identify the bridge elements between both communities by stepping back in the bigger “mention” component.

Figure 11 - How the two separated components are connected (extract from the “mention” connected component

Communities in Twitter: conversing

Now that we have focused on how people connect (or not) together in the community. We can focus on what they interact about.

The idea is to compare the semantic space in which the individual exchange when they discuss together. Is it different from how they mention each other? and how? and what brings them together.

To do so, we have built a different network, it’s actually a network in which links materialize the hashtags exchanged between two users. It has the same flat topology as the previous network of people, but it is rich of the semantics that people use when they converse.

Figure 12 - Illustrative example of associated selection between people (left) and hashtags (right) with detangler to promote exploratory network analysis.

Using this model, we can capture the schemes of conversations and see how hashtags bring the two sub-communities together.

If we limit ourselves to the semantics of engaged conversations, very little is bringing the two communities together but #ecoc2019. The interest of each community are clearly identified and separated. One group is really focused on travels in Matera, whereas the second group is centred around the Lote4 event, transfer and openness.

Figure 13 - Communities of people replying to each other. We can see the two disconnected group of people on the left, aligned with their corresponding hashtags on the right (people on top, converse about topics on top). The frontier is very clear. People on top discuss of lote4, and people on bottom of Matera in terms of travel.

However by looking at how these two groups are mentioning their members together, the frontier still exists of course, but it is by far more blurry, and user Matera2019 seems to be an interesting gathering point between these communities.

By the way, we have analysed that, even if #matera is the heaviest most occurring hashtags in our dataset, #lote4 is by far more often more co-occurring with many other hashtags putting #lote4 as the most influent hashtag in terms of group cohesion. In other words, the sub-community centered on Edgeryders is more cohesive because people discuss more often about the same focused topics. Could it be an interesting side-effect of an effective moderation? Or could it be that semantic cohesion makes a community really a community?

Figure 13 - Communities of people mentioning each other. We can see the two disconnected group of people on the left, aligned with their corresponding hashtags on the right (people on top, converse about topics on top). The frontier is more fuzzy and a core set of topics (on top) relate very much to #lote4 whereas #matera is very diffuse (on bottom)


This analysis is only the result of a 2-day workshop, and we would wish to push it further to have a complete understanding of the structure of the community. But it is a nice example how we can dig effective elements of discussions, topics of interests, and central people, at the heart of a very noisy spread Twitter conversation.

Also, considering the context of the event it would make sense to filter the data after, the end of November. Later tweets refer to Matera or to the unMonastery, but at this point the two terms no longer refer to a unity. There also could be many other questions, focused on data properties but also wider openings: How would we compared and define that with annother community, such as Imagination4People’s mailing list / communities? What is the specific role of these [put-your-list-here] individuals? What were their focus of attention during [put-your-timeframe-here]? etc. etc.

Now that we know all these tools and metrics are available, the most challenging task would be to set a (Cartesian) method to systematize and build integrated tools for the analysis of such a Twitter community. Here are some examples ways of applying this to the real world:

  • Showing the graphs on events to demonstrate which were the main topics tweeted during the day and which people were most active - this implies that the graphs are developed rather quickly
  • You can identify people who are especially interested in specific topics, at least if mentioning the topic in \# is a good indicator for this
  • Really cool would be if this could be used instantly on a twitter wall during events
  • By reducing the overall number of members of a community to the very few (18 in this case) really active ones, you know who you would need to approach for future events, discussions etc. > find the champions
  • As it comes down to few topic \# on our graph, we might be able to encounter new emerging topics or other unexpected stuff


All the data is made open from the Masters of Networks 3 drive. This analysis has been processed during the event, Tulip 4.6 has been used to process the CSV data, and build the initial networks, detangler has been used for the paired semantic analysis, and a little bit of d3 to process the time series (just a bit after the event).


Some thoughts

Hey, nice summary! I’ve thought in a similar direction I believe, so I’ll throw in some comments and see what sticks.

Re You can identify people who are especially interested in […]

Here is what I tried to do at a multi-day p2p event:

Most people did not have a connection with each other when they came, so the idea was that you put post-its on the topics if you would like to discuss it something (it failed, probably for more than one reason). That is relatively similar to your twitter wall - correct? I used a “soft cartesian sorting method” based on two broad categories (+antonym) the stuff falls into.

I think it actually makes most sense BEFORE an event physically kicks off (say during travel to the event) to do a lot of meet & greet stuff on twitter. During the event I would try to push people to establish milestones or follow-ups for AFTER the meeting.

Re interested in specific topics

I’ve tried to build a mock-up interface for documenting generic projects in a way that interfaces nicely with a network framework. I think the interest is usually greatest for a combination of topics + a specific purpose (i.e. a project). Here the purpose is to improve the documentation of projects in hackerspaces and fablabs (I have more detail on this topic if you are interested).

The gray font symbolizes a sort of coarse mind map (connections currently not drawn) that is stretched according to the phrases you use to sort this stuff along XY axes (these could also be changed dynamically of course). The black font is for the “high level work packages” (in PM lingo). You could explode them into more detail (many more specific phrases) to see what has progressed how far, where there are obstacles, comments, and of course who is working on what currently. The greyish wedges are currently just placeholders to symbolize, say, 2 x 500 man-hours that two initially separate groups will probably have to spend coming from the theoretical, as well as the practical side of things. The degree of blurriness could symbolize the confidence in the prediction. It is essentially somewhat similar in thrust to what @Ola has written here, and I hope to be able to “map” these dynamics effectively on this basis.

As you can see I try to move away from looking at nodes (people) as agents and focus more on a project with impact (and perhaps certain edge structures) as attracting more or less efficiently the necessary resources (connectors, man-hours, resources, etc.). I am aware that people probably remain the most important element for some time to come - but a) people are non-digital (an twitter is IMHO a very small, very unrepresentative subgroup) b) the change in perspective may allow me to perceive things that are almost hidden otherwise. I think that is pretty much what you were doing when you looked at the 18 “champions”. Of course the project needs to interface effectively and efficiently with people. If you have accumulated some machine readable data that is project relevant, I think a pretty detailed and comprehensive plan could be produced, that would easily pay back the 5-15 minutes it needs to be produced. Basically you would drag a default suggestion into the shape that makes most sense to you, add relationships (e.g. prioritize, make milestones & gateways, timings), swap some phrases, and watch what promising connections this would find via twitter (or linkedin etc.). If you have such a tag and relationship rich plan that can be viewed using various perspectives (“cartesian coordinates”), or mapped onto social networks it becomes extremely easy for potential collaborators to hook up with you, or perhaps coordinate one of their milestones with you to produce synergies.

Re you know who you would need to approach for future events, discussions etc. > find the champions

I would be careful with that conclusion because this is what I already see a lot happening in real life, and it is a very mixed blessing. Well meaning people are quickly drowned under a deluge of requests. Good work begets more work. The devil sh*ts on the biggest pile. I think what you want to do is find the 2-3 people connected to the champions who have the potential to BECOME a champion themselves (through a specific topic). Finding and “harvesting” the champions out of a community does not create more value overall. Of course if you target them in order to empower them more (or give them compatible sidekicks) - you are quite right.

you know who you would need to approach for future events, discussions etc. > find the champions - See more at:
Really cool would be if this could be used instantly on a twitter wall during events - See more at:
Really cool would be if this could be used instantly on a twitter wall during events - See more at:

More thoughts and some links

Have you considered looking at community through a ecology lens? Online communities work somewhat differently but I was missing the different forms of interaction in the discussion a little.

Also from my experience I the concept of lacunarity in combination with this the metric discussed in this paper helped getting a grip on related phenomena. The idea is to get away from groups and embrace clusters. Quasi-groups of course still exist - which are then defined by (relative) absence of connections. Modularity does this in a way, but it always seems a bit of a blunt tool for the job. It is still useful as it is relatively intuitive and simple I’d say…

Thanks @Trythis

for all the the comments and references!

Well, there are indeed many levels to network analysis, and we’ve merely scratched the surface of this kind of analysis on a few days of hackathon.

We have also adapted the questions to the data, as it should be the other way around, real questions bring data capture, then it’s way better to devise proper answer to the question. However, this study shows how by looking at different angles all at once through networks, temporal and semantic analysis we could question different notions of what makes a community, a community through the sole lens of Twitter.

Now you’re right, if efforts are made beforehand on Twitter, then a lot can easily be achieved IRL at the day of the conference, and we could question if activity on Twitter could predict partly some real-life connection, but also the ultimate purpose these conferences is often to foster these ‘active’ social contacts.

As for your map it’s a bit too conceptual for the geek that I am, but I would say, if you can categorize the interactions between your peers along with the same categories you are mentioning (and qualify links of your network with them) you should be able to retrace the path you propose.

You propose to combine even deeper such analysis with many other characterizing relationships (not limited to twitter and/or digital info) and I would completely agree with that, I think this is the future of SNA, and we’re right at its edge now. So remember, even if the raw material we used here is rather limited, we only analyse people based on their twitter activity if they were captured by our simple Twitter query (no deep prior knowledge on people or on the event have been used to produce the analysis). Bottomline, we’ve found groups at the heart of the giant Twitter hairball that seem to be the supporting frame of the whole network here, and we haven’t yet tried to cluster the data and analyze each subgroup.

What we may want to retain from this analysis, is:

  1. -For data scientist like me- how insightful can be a multivariate relational analysis (social, time, semantics…), and how badly we want to correlate observable variables with that.

  2. -As a ‘real-world’ result- These ‘champions’ are more than just individuals (or superstars), together they are a ‘champion’ network. And this difference is critical I think. That is, by their common action, they are fostering many reactions from the rest of the network. Each individual could have they own ‘followers’ but themselves, they are the seeds who can gather even more individuals, and bring long lasting support for the community to exist as is. Anyone that would be picked and included in this core network (not just one individual, but interacting with a few in this subgroup), would become a new member of the community, and may in turn become another core member.

Well, I think this confirms your last point! Thanks for the tip on the ecology lens, I’ll give it a shot.  As for clusters, well it’s another very long discussion, I try to limit it as much as possible as their is no perfect clustering algorithm, and the definition of the boundaries of what forms a cluster always need to be discussed. Of course, we always need clustering with large data that we can’t untangle at once… but again, that’s a different discussion!

Thanks a lot again,

(19-05-2015 edit: typos)

1 Like


We do networks because nodes are never “just entities”, they are entities in a network. The emphasis of connectivity (and, by implication, away from identity) is what drew me to network science. You can get interesting, sophisticated system-level behaviour out of identical nodes and randomness, and this is a marvellous thing to behold.


I took the liberty to reformat the post, freeing it from the scourge of copy-paste into the WYSIWYG editor. smiley