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A Blog recording the life a Web Scientist
The Web is a ecosystem of machines and people interacting, and these interactions form and produce the various forms of Web activity. Whilst it might seem that the affordances of the technology enable us to create and realise various different types of activity (online shopping, social networking), the technology itself is socially shaped and developed. Although not explicitly stated or defined, this co-constructive relationship between technology and human activity is the underlying socio-technical protocols of the Web, it is the protocol that exists (but not via design) to enable the Web to grow, and ultimately, function. It is important to note that the word protocol is not to be considered deterministic, but rather as a ‘fuzzy set of interactions’ that have evolved alongside the technologies and human creativity.
When we analyse, describe and predict changes about the Web, we often gloss over the social components of Web activity, and tend to steer towards more positivist approaches to measuring change, growth, or decline. However, what if we were to define such insights by examining it via a socio-technical protocol, or better still, can we derive characteristics of socio-technical interaction on the Web in order to better understand our interactions and intentions? Rather than trying to build upon existing methods of modelling and prediction, can a new form of analysis be derived?
Research is already showing that there exists replicable patterns of activity on the Web (take for example work related to network structures, burst patterns, and communication diffusion), is it possible to take this technologically focused approach and reconsider the social?
Ultimately, the future of the Web relies on both people and technology, and the socio-technical protocol that emerges from their interactions.
The following post provides a summary of the activities and research that was presented at the Web Science 2014 Conference, held in Blooming, Indiana.
Web Observatory Workshop
The Web Observatory workshop (Link), which was led by members of the Southampton SOCIAM team built upon the past three workshops and various research activities related to the ongoing efforts of building Web Observatories. The emphasis of this workshop was for current research that described the implementation and use of different types of Web Observatories, how they are built, and what they are being used for. The workshop contained 5 presentations which was followed by a round-table discussion focusing on the best practices and next steps required to interoperate between Observatories.
Professor Noshir Contractor gave an excellent opening keynote, with a strong emphasis on methods, and the benefits of making data available Web Observatories. Along with the advantages described, Noshir made the excellent point that we need to be aware of analytical dangers, and we need to make sure we are not looking only where the data is, we need to expand beyond the traditional watering holes (e.g Twitter). Complementing Noshir’s argument, Siripen Pongpaichet’s (Link) presentation of EventShop demonstrated how we need to think about data beyond the Web. EventShop provides a platform to draw together Web and environmental data in order to provide richer insight into real-world conditions. However, this raises the question of the boundaries of Web Observatories, and what types of data should we be dealing with? Should we be trying to cater for this type of data as well? Whilst there was a strong agreement that this data is important, there was consensus that the initial focus should be on a number of core datasets (whatever they may be), and then place an emphasis on trying to bring together other datasets. Following this, Thanassis Tiropanis and I gave a presentation on the Web Observatory work under way at the Web and Internet Science group (WAIS), and within SOCIAM, describing the current efforts in developing a three tier architecture which separates the data stores, data cataloguing, and analytics/visualisation components (Link). Over the last 9 months there has been much effort invested into developing the necessary infrastructure to support storing, accessing, and querying large-scale Web streams, which an increasing emphasis on offering streaming access and analytics across multiple streams of Web data.
Another strong theme that ran though the presentations and round-table discussion was the strong push towards using a common schema to describe the datasets that are going to be listed on the Web Observatories around the world. The only way that this is going to grow beyond the single silos of institutions, organisations, and businesses is that when we list our datasets, we use the common schema. There has already been a lot of work on this, and we have developed a common schema that is now available on schema.org (Link).
The discussions that were had in this workshop were echoed throughout the main conference, where there was a lot of discussion around the use and availability of data, as well as what type of methods can be applied to these various forms of data?
Web Science Main Conference
Following a full day of workshops, the main conference consisted of a 3-day, single track programme, comprising of morning keynotes, followed by a series of presentations of varying formats including pecha kuchas and standard presentations. I was fortunate enough to have two submissions accepted and have the opportunity to give a pecha kucha and poster to talk about the ongoing Citizen Science research, and a 15 minute presentation, in which I presented the current research on sociological methods for Big Data, with my colleagues, Professor Susan Halford, and Olivier Philippe. The poster presented the latest findings of the analysis of citizen scientists and their activities and engagement as a community. Following a stream of studies, the findings of this analysis discovered the clustering of participant discussion forum activity on the Galaxy Zoo forums, which may correlate to specific motivation of participation.
The second paper presented discussed the methodological challenges and potential solutions to engaging with Big Data sources whilst appreciating the complexities and pitfalls of quantitative big data analysis. The main argument and contribution of this work is showing that mixed methods, even when dealing with extremely large sources of data is essential for understanding the nature of the data, and to avoid the ‘Twitterology’ of misrepresenting the data or the context. The presentation resulted in a good level of discussion around the role of interdisciplinary thinking and methods, with a strong agreement that we (the Web Science community) continue to engage and develop methods that bridge across disciplines in order to better understand what we are observing. In fact, Daniel Tunkelang’s opening Keynote was a great start to the conference, as it was truly inline and with the topics and themes that were ripe for discussion throughout the conference. Daniel discussed issues of big data, methods, correlation and causation, human interaction, and understanding human behaviour beyond the aggregated counts offered by statistics.
One of the core themes that persisted throughout the conference presentations was the discussion and analysis of different Web systems, their behaviour and characteristics. Many of the presentations focused on specific ‘features’ of Web systems such as Wikipedia, Facebook, Tumblr, Twitter. As a nice summary, Harith Alani’s presentation on “Mining and Comparing Engagement Dynamics across Multiple Social Media Platforms” (Link) mapped the studies related to the various systems that the Web Science community have been analysing, illustrating how the number of systems analysed has grown since the initial Web Science conference. Whilst this is not surprising given the growth of these systems, it is interesting to see the focus around specific social machines such as Twitter. This raises a number of questions regarding our focus; are we still placing a lot of resources analysing these systems because they are easy to access, is it because they are the most successful (and what is the definition of success), or is it because we are still trying to develop methods to better understand the systems and the data? These questions are extremely relevant to SOCIAM given that we have focused on similar topics in SOCIAM for quite some time. The research presented is extremely useful and to the work we have been doing in trying to classify the behavioural (and observational) characteristics of social machines. This type of classification will not only help provide generalizable features of different types of social machines, but also provide us with a framework to help observe them. However, whilst we are focusing on the successful and high profile social machines, we also need to be aware of those that are hidden, or failing. What are the characteristics of those social machines that do not gain popularity, large user bases, or obtain substantial interaction with others in the Web eco-system? Something yet to be understood.
On reflection, Web Science 2014 was a great conference with a vibrant and growing community. This year there was a surprising amount of buzz around the need to overcome Twitterology and the Big Data hype; interdisciplinary methods was the message of the day. In SOCIAM, we are always keen to present the 4-quadrant diagram of people vs machines (see below, courtesy of Professor Dave De Roure), and we consider social machines to fall into the top right quadrant, more people, more compute. We are now at a point where we need to consider this in terms of methods. We are well aware that big data =/= large scale (quant) methods, nor does small data =/= small scale (qual) methods. Whilst this requires a much lengthier discussion regarding the epistemological and ontological boundaries and limitations of both the quantitative and qualitative paradigm, perhaps we are working towards something as illustrated in Figure 2?
A short summary and reflection of the Wikimania social machines hack event which happened on the 24/25th May 2014 in London.
The Wikimania Social Machines hack fest was a fantastic forum to initiate the discussion around the notion of social machines outside the core community of SOCIAM and Web Science researchers. Comprising of a two-day event, the first day focused around getting to know the attendees and their interests, which directly fed into a series of video conference calls with a number of key members of the Wikimedia organisation. Given the varying ‘Wikipedia’ knowledge of the attendees, the conference calls were an ideal opportunity to understand how Wikipedia operates, and get feedback from those outside the core community of editors and administrators.
Perhaps not so surprisingly, the topics discussed during the video calls revealed that Wikipedia is extremely complex, and even some of the core members of the community did not know all of the smaller processes, policies and idioms that embody and govern Wikipedia. The insights offered by Wikimedia’s such as Philippe Beaudette, Aaron Halkfaker and Fabrice Florin told a story of a complex socio-technical ecosystem of social machines. similar to other environments we have been studying (take Twitter for instance), Wikipedia comprises of multiple social machines, each with their own human-machine processes, goals, motivations, and outcomes. From an observational perspective, understanding Wikipedia becomes an issue of granularity (this is something that really needs to be unpacked and understood), whilst it is possible to observe and analyse the system as a whole with metrics such as page edits and views, does this mask over the more subtle processes that are critical to the operation of Wikipedia?
The teleconferences and demonstrations by Ed Saperia and fellow Wikimedia representatives offered some really interesting views on how Wikipedia is being used far beyond the original design of an online knowledge base. Wiki-projects, badges, rating systems, Wikipedian profiles, Wikipedia tools, bots, talk, these are all emergent phenomenon that had grown from the initial idea of a collaborative environment where anyone with access to the Web could add and modify a Wiki page. The development of these socio-technical processes are the emergence of new social machines supported via the Wikipedia platform (which itself is a socio-technical system, supported by human and machine interactions). Take for example Wiki-projects, these are entire communities within the Wikipedia platform which emerge around a given topic or set of build around. We were shown the Medicine Wikipedia project, which comprises of a large number of medical articles in over 250 languages, produced and curated by an growing number of individuals, from medical students to practitioners. Within this project, which itself is a Wikipedia of medicine are mission statements and project goals which drive the progress of article quality and completeness. This progress is supported by the community, who engage with constant discussion using the ‘talk’ function, where new ‘users’ are greeted and introduced to the project.
However, ‘Wikiproject Medicine’ is not a unique case, there are hundreds if not thousands of emerging and active Wiki-projects. What makes this interesting is understanding the initial inertia for the formation of this project, how these projects attract new members, and how they actively sustain the commitment of the community. We also need to consider how these communities are developing their own technical and social mechanisms to support their work, and how these become adopted by other communities and projects over time. For instance, the medical project has developed a number of custom templates and navigation bars, which overtime may be adopted by other projects. There has also been consensus to form an organisation structure consisting of departments, participants and task forces, illustrating the formation and alignment of social processes.
Tying this back to the studies we’ve been doing in SOCIAM and citizen science analysis, a great cross-over is examining the relationship between talk and task (editing) (although as Ed pointed out, one user’s talk is another user’s task). Our analysis of citizen science projects have shown that like Wikipedia, online discussion between participants have become an emergent phenomenon, offering the community a means to chat, and in some cases, discover and confirm new scientific insights. What we also observe is the relationship of those talking, and those tasking, and typically, with a strong positive correlation between active talkers and taskers. Whilst Wikipedia is less prescribed in terms of an individual task and workflow, is it possible to observe the same talk-task characteristics identified in other online communities?
There is real potential here for SOCIAMers here, working with Wikipedia provides us the opportunity to actively observe the emergent phenomenon that led to the creation of social machines, and could prove to be an suitable environment for us to test out theories and mechanisms to support and potentially build new social machines.
As part of our quest into understanding the emergent properties of social machines, one avenue of current research is the study of citizen science. In recent years, this Web activity has become a great exemplar of crowdsourcing and “Wisdom of the Crowd”, where thousands – and now millions – of volunteers spend their own free time completing scientific in order to further our current knowledge in many different domains. Galaxy Zoo, which could be considered the poster child of citizen science, illustrates just how successful these systems can be; attracting millions of individuals to help classify the shapes and types of galaxies, GalaxyZoo offers citizen scientists the ability to contribute to a meaningful cause, whilst simultaneously gaining and advancing their own knowledge and expertise. Such successes have been witnessed outside the domain of astronomy, including projects which ask volunteers to identify wild life within the Serengeti, listening to the sounds of Whales, or the discovery and identification of cancerous cells for medical prognosis withCell Slider.
Underpinning all these citizen science projects is the amazing work of the Zooniverse team, which along with their Zooniverse citizen science Web platform, provide the foundations to kick-start a project off. Over the last 5 months, SOCIAM researchers have been given the opportunity to work closely with the Zooniverse team in order start to analyse this Web phenomenon, asking questions about user participation and engagement in order to answer the bigger question of how does the social machine of citizen science function.
To date, our research into citizen science through the lens of the Zooniverse platform has provided us with some very exciting findings in respect to the interaction and activities of citizen scientists. By studying such a rich dataset, we have been to witness how project domain helps shape user participation, and how citizen scientists develops expert knowledge, and most impressively, how the wisdom of the crowd functions as a project evolves.
In line with other research in SOCIAM. we have also been gaining deep insight into the design and deployment of citizen science projects, with a particular interest into how does one create a socio-technical system that encourages and sustains participation of people. Unlike recent articles posting about citizen science as activity for ‘online gaming’, our studies, as well as others (Bowser 2012; Iacovides 2013) are finding that the gamification of citizen science is not only wrongly defining the purpose of these systems, but also harmful to their participation and success. Studies (Raddick et al. 2010, 2013) have shown that the core community of citizen scientists are far from the online gamers described in such articles, they are a community of individuals who which spend many hours discussing, classifying and helping others.
As our work continues to develop, we are looking at methods to better understand how citizen scientists behave, and how teams like Zooniverse can continue to attract and sustain a dedicated user base.
As this was my second World Wide Web conference now (the first being Lyon, France, 2012), I has some expectations of what WWW13 would be like; in terms of the community, the papers, and the sheer energy of such a large conference. Rather than providing a day-by-day account of the conference and various activities, I’ve decided to highlight the aspects that stood out the most:
With all that said, I’m going to talk a little more about the ideas and research that came out of the two workshops that I attended and presented at, both the Social Machines (SOCM) and Web Observatory (WOW) workshop. As these are very close to my PhD research and general areas of interest, I feel like they offered a great opportunity to share current research paradigms, directions and potentially future work and collaboration with the community.
Social Machines Workshop 2013
The first workshop, held on the Monday was SOCM, which was focusing on the growing variety of research surrounding the theory and practise of social machines (a term that received as much positive debate and discussion as the papers presented). The workshop, which was supported by the SOCIAM funding at Southampton University, provided a great forum for the current cutting edge and novel research that was involved in understanding how social machines of the Web (for now, let’s says a socio-technical web-based system) operate, and how we could potentially build and guide them. What was really interesting about the papers that were being presented (included my own) was the diversity of the theories and disciplines being drawn upon; to this point, Professor David De Roure made a great observation that this is potentially an evolution of Computer Science, from just designing systems in terms of the engineering, we are now dealing with a world where the design of the system is inherently human as it is technical, and as a result of this, we need methods, tools, and theory to understand how to better design these systems. However, unlike traditional design paradigms and approaches, we are no longer dealing with developing software or systems, at the micro level, the Web has offered us a platform to create something that is global, and as a result of this has many societal, economic and political implications.
This really brings me back to my research and interests, and the papers that were presented offered a great set of insights and discussion points in terms of what the Web actually is, and how can we understand it in terms of it being socio-technical, and what insights does that provide us (developers, individuals, or the collective ‘society’). There is also the issue of how does one define a social machine; what makes it social, and what makes it a ‘machine’? At the broadest of scales, everything could be considered a social machine, the interaction between a human and a hammer is inherently social and technical, not only in its use, but the societal needs for the hammer to be constructed, the shaping of the hammer because of the tools available, and the use of it (does a hammer have to be used just for nails…?). It is very easy to go down this route of saying everything is a social machine, so I think in the next few months we will be thinking very hard about what the boundaries and scope of this is. For now, I offer a few characteristics (which rose from the discussions surrounding my presentations) for what a social machine could be characterised as:
These points are very much at the heart of the discussions of understanding what we mean by social machines, and through some more discussions, debates and empirically-driven research I think we will be much closer with a definition, a set of characteristics and potentially a framework to fully classify a spectrum of socio-technical systems that we consider as “social machines”.
Web Observatory Workshop 2012
The second workshop that I attended, WOW2013, focused on the efforts of those interested in the Web Observatory – an international WSTNet project which is currently gaining the support of those interested in providing a platform for academics, industry, researchers and government to store, and shares their data. This ties in very well with the research of social machines, as an essential part of being able to understand them and monitor them is to have a platform that can collect the variety and volume of the data that they generate. As like the previous workshop, the papers presented were of great variety, from the techniques and current approaches to creating a platform, the current usages of individual Web Observatories, and the incentives and design considerations of building and using them in a distributed manner.
The one aspect of this workshop which ties into my own research (and the section of the workshop that I was presenting in) was the use of different social machines to track and understand the diffusion of information and viral content on the Web. My research (which was the result of joint collaborative efforts with KAIST, South Korea) looked at how Wikipedia could be used as an indicator of soon-to-trend human activity, with a specific focus on how information is spread across different countries, and the implications of culture on this. The discussion which followed my presentation led to the potential collaboration with L3S, one of the partner WSTNet Labs in Germany. These kinds of outcomes and future collaborative work are only made possible by having these kinds of workshops along with the right community attending.
There was also an interesting discussion to be had with regards to Paul Booth and Paul Gaskell’s paper on looking at the exchange of value for data. At the most simplest level, they proposed the question of why would people want to share their data, what are their incentives and their rewards for doing it. A nice way to look at this is a market place for data; individuals who have datasets partake in this market place, the data – valued by some index and set of metrics – can be traded between individuals, which then creates a data index (like a stock market for data). This raises some questions of commercial vs. non-commercial markets, competition, or even more fundamental than this, how does one value data (especially as this will encounter a cold start problem). The concept of a data index and trading place is a great idea, however, I think we are going to have to think hard about how can a piece of data be valued, especially when it is first introduced into the network. Valuing it solely on the size of it, or the number of records has many issues, therefore the data on the market needs to have some specific meta-data associated with it in order to assign value to it. There is also the issue (and differentiation needed) between uploading a piece of data, downloading it, and actually using it; the latter being the most hard to quantity and track. Take data.gov.uk for instance, government departments can upload their data, then individuals can download it. However being able to track where this information is being used, and for what purpose (i.e. Applications, Website, etc.) is not simple. Their needs to be some technical intervention to enable some form of feedback loop for tracking data use.
There was also a lot of cross over and synergy with the discussions had at the previous day’s workshop on social machines and how the development of a Web Observatory will be an essential addition to understanding how social machines develop, how they evolve over time, etc. What is interesting to me is the ability to not only track these machines in terms of their macro characteristics (i.e. the network structures and the amount of data that they are providing), but also providing some way of understanding them at the micro level as well, in some sense, providing the meta-data around the captured quantitative data in order to add context to what is being observed. This goes back to the discussions regarding understanding these social machines in terms of the human activity, as well as the technological development. In order to understand their evolution (or even just the way they are being used), then we need a way to capture both the macro and micro, simultaneously. There is a lot he issue of how does one capture multiple social machines, and their interactions between them? What are the boundaries that exist between one social machine and other, and when capturing this data, how does one represent it? These are all questions that need a lot of further discussion, and I think it they will be answered not only by theorising, but by practise as well.
My training in the social sciences has told me that not everything is about building things, and I believe this is completely true. However, when we want to build something (which at some point, will be inevitable), understanding of the implications and interactions with the social is just as important as the technologies that underpin it. The construction of a social machine (if that is even possible), will social understanding, technological expertise, and tools such as the Web observatory to track and monitor its progress.
The past week has been a great experience, catching up with those that I met last year in Lyon, and meeting a few new! It has also been an excellent time to discuss the ideas which we have all been interested in during the last 12 months here at Southampton. Not only has WWW2013 provided a forum for the cutting edge Web research, it has also been a great forum for Web Science discussion, which hopefully will continue to grow, especially with the WWW2014 Web Science track.
May 1st 2013 marked the first Web Science Social Theory workshop, held at Web Science 2013 in Paris, France. As part responsible for organising and running it (credit goes to Lisa Sugiura who was focal in organising and putting the workshop together, and also Huw Davies!), I felt like today was a great success in terms of the number, engagement, and sheer enthusiasm that both the participants and presenters had.
The morning opened up with Dominique Boullier who gave a seriously fantastic talk entitled ‘Social theory and technical architectures: 3 stages and many traps’, in which Dominique provided one of the most comprehensive accounts of how social theory has evolved, and how this relates to Web Science. Covering: Durkheim to Latour, Berners-Lee to Kleinberg, Dominique’s keynote was something that I know a lot of Web Scientists wanted to hear; the talk raised a lot of discussion, with the audience engaging in topics from methodological and epistemological challenges, to the practicalities of applying social theory to an ever changing Web. Undoubtedly, this keynote set the tone for the rest of the workshop.
Following Dominique’s keynote, the first of two paper sessions commenced, which included from topics such as the use of social theory for understanding the Arab Web and the development of a model for interdisciplinary teamwork with the use of social theory. Both presentations raised some important issues regarding the implications of using social theory, and the challenges that are faced. Sabrine Saad et al. (presented by Stéphane Bazan) disucssed issues regarding the ‘Laurence of E-Beria’, a term developed to label researchers that study the Arab Web, but are not familiar with the culture and practices of the Arab world, thus interpret the results incorrectly. Some really important points where made here, and demonstrated the use of social theory as a way to begin to understand some of the differences in uses. Peter Kraker’s paper on interdisciplinary working also raised the know issue of the divide between the computational and social sciences, an issue that has been brought up before in Web Science; and as a working solution, proposed an iterative process in order to collaborative as well as cooperate together.
After the (much needed) coffee break, a second panel session which featured 4 great talks was given, including Jess Vass’s social machines and social theory paper, offering a new take on how social theory can be used to conceptualise and understand the outcomes of a social machine. This was followed by Kristine Gloria giving a talk on how Foucault could be used to understand the Semantic Web, and how the use of social theory can be used to unpack the different forms of truth that are constructed (and hidden) within a Semantic Web Ontology. Olivier Philippe and Jen Welch then presented their work on looking at the methodological implications of using Big Data within sociological research, taking examples from recent studies of Twitter and how they offer only shallow meaning and representations of what the data may actually represent. This echoed the discussions during the morning’s keynote by Dominique, and the examples provided really brought this to life. Finally, our last presentation was given by Fabian Flock, who discussed the possibility of using media related social theory to understand the role of collective intelligence on (in) the Web; this was great as it approaached the issue of social theory from an angle that previously had not been discussed (or considered).
The final session of the workshop was a 20 (but went onto 40!) minute discussion which featured Susan Halford, Stéphane Bazan, Craig Webber, Dominique Boullier, and David Beer, who despite Skyping in managed to give a short, but extremely relevant keynote, as well as take part in the discussions. Some really good (but hard) Web Science questions were being asked, and in return were answered with passionate responses. With discussions varying from the application and integration of social theory into Web Science, to the specific details of combining new forms of data with existing social theory, much ground was covered; we even managed to question the role of Google glass for future Web Science research.
Overall, today was a great day for Web Science. We all took part and contributed to many great discussions, and the room was filled with a collection of highly engaged, enthusiastic, and excited researchers who we hope now are inspired and charged to carry on the great research that they are already undertaking.
I would like to thank all that came to the workshop and also those that presented! Bring on Web Science 2014!
As before some key facts about the Twitter conversations at CeBIT during the past 3 days:
As before we are seeing similar spikes in the communication activity during the day, with a number of key announcements and events proving to be important enough to cause a sudden rise in activity, indeed, the announcement of the real-time cybercrime mapping by Deutsche Telecom was a popular topic within the Twitter communications. The news of the top ‘Nine online marketing trends’ was also popular, but not only in terms of just announcing the news, but also causing communications between different users (in terms of mentions).
As before, we are seeing the influence of specific users within the network continuing to grow, namely ‘Cebit’, ‘Code_N’, and the disconnected conversations of ‘Nienor_’. As before, we are seeing users taking the role of drawing together these different streams of information from these highly retweeted users, such as ‘eklaus’, or ‘CeBIT_Japan’.
Taking another look at the network, we are also able to explore the flow of conversations, or simply, how the tweets were retweeted overtime. As the Figure below illustrates, the speed of diffusion of the tweets and their size differ dramatically (only chains of larger than 20 are shown), with the vast majority of them (99.6%) forming chains of less than 3 retweets. Does the content of the tweet or the person that it originated from depend on the speed and size of the chain? More to come on this later!
As before a quick summary of the Twitter conversations during the first two days of CeBIT 2012.
An overview of the Tweet timeline with some key events:
Since day 1 we have anumber of new hubs of information forming, and also indivudals (such as ‘Michael_Espina’) who are relaying information from ‘cebit’, including event information and other users who are connecting different conversations streams together, e.g. ‘eklaus’.
More to come for Day 3!
Here is a quick summary of the Twitter conversation stream for #CeBIT during the 5th March 2013.
An overview of the Tweet timeline with some key events:
Here are the key individuals within the twitter communications network after day 1, the hubs represent nodes represent those that have received a high number of retweets, but perhaps more important to note are the ones connecting these ‘influencers’ together, such as the user ‘abcforum_hh’. Let’s see how this develops tomorrow!