blog - MUHAI

Study without ChatGPT… to work more wisely with AI

2024-06-24T12:57:37+00:00

Paul Van Eecke (VUB)
Katrien Beuls (UNamur)
Tim Brys (VUB)
Adapted by Folco Soffietti (VIU)

Source: Pexels, modified by Folco Soffietti

Adapted from the article “Studeren zonder ChatGPT, daarna verstandiger werken met AI” published on the Knack on the 9th of April 2024.

The Integration of AI into curricula cannot neglect a sound domain knowledge, good language skills and thorough knowledge of the scientific method.

In Belgium, the Rector of the University of Gent announced that from next academic year, students will be allowed to use generative AI in their thesis. This sparked a debate within the academic community, questioning the purpose and methods of university evaluation in the age of AI.

Generative AI is supposed to be able to reshape every aspect of the research cycle, but is it so? MUHAI’s researchers can help you dive a bit deeper in the matter.

Generative AI systems such as ChatGPT can produce texts that are sometimes indistinguishable from those written by humans, but still have difficulties in imitating logical thought processes. Even so, let us assume that generative AI can still replace important elements of the research process. The detection of AI use is becoming increasingly accurate, just like plagiarism. And what student will risk losing their degree due to fraud, possibly even years after the date?

Even so, we can still make the exercise of assuming that it is not feasible to ban the use of generative AI. Then two paths open up: allow AI use or discard the thesis. Contrary to the Rector of the University of Gent, some professors consider that understanding and processing of knowledge cannot be outsourced to ChatGPT when studying. The risk is, in fact, to no longer acquire these non-negotiable basic competencies. They are crucial in an academic education. Therefore, if the use of generative AI cannot be restricted in practice, it seems that the master's thesis shall be replaced.

Until recently, the master's thesis was the culmination of a long educational curriculum in which a set of skills and understandings were gradually built up in students. In general, we do teach skills for which there exist digital tools that can perform them better or more efficiently. For example, we still learn spelling in primary school, even though spell-checking programmes exist. Why? Because spell check only makes sense if you can already spell yourself and write an intelligible text. The same with arithmetic: we still learn arithmetic rules because a calculator is only useful if you have the necessary mathematical understanding yourself. These tools can help us avoid mistakes and make our work more efficient. But if we cannot interpret the output of these tools, they are completely useless.

It is no different with generative AI at the master's level. In a master's programme, a student is expected to grasp professional literature, acquire research skills, develop critical thinking, and so on. Only someone who has already acquired these skills to a considerable extent can use generative AI responsibly at the level expected of a master. How else can that person interpret what the AI system produces? An incompetent writer cannot judge the quality of the generated text, let alone improve it.

if we want our students to be able to use generative AI competently and responsibly in the workplace, they will still have to acquire the basic equipment of the master's degree. Shortcuts and distractions of generative AI will have to be ignored for a while with the higher goal in mind: being formed into a competent expert in the chosen field of study.

If the master's thesis won’t be a reliable way to test academic growth, there are fortunately alternatives. Evaluating throughout the year, or final exams. Either way, generative AI should not mean the end of the goal of shaping students broadly and scientifically. And this, regardless of a specific tool.

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.3

2023-10-06T09:19:18+00:00

Carlo R. M. A. Santagiustina, Venice International University.

-Continues from Part 2

To answer the questions raised in the previous chapter of this article in a synthetic way, here follows a short-list of major implications of the aforementioned changes to data access policies. To grasp the societal impact of these changes, I selected two examples of social media data-based and community-centric projects. Today, with the current social media policies, the collection of user-generated data required by these projects would have been impossible to afford. Both projects are ongoing and are related to -and contributed by- MUHAI. The first one, called AquaGranda: A digital community memory, started in 2020 from the EU project Odycceus, after the extreme tide of 187cm that flooded Venice in November 2019, and progressively grew involving other EU projects and research institutions, such as MUHAI, and recently received an Honorary Mention at ArsElectronica, for the European Union's Citizen-Science Prize. The other one, called MUHAI Inequality Observatory, started in 2021, and is being developed within our consortium by a multidisciplinary team of researchers. Both projects focus on topics that are undoubtedly relevant to a wide audience of citizens and institutions, respectively, the impact of extreme weather events on social conflicts, and the understanding of the public perception of social inequalities and intersectionality.

Democracy wanes – access denied, our cause remains

For projects like the aforementioned ones, free access limitations to user-generated data from social media platforms, such as Twitter, can produce detrimental effects that, besides damaging scientific research, can directly affect communities and their economies at multiple levels. By limiting the generation of externalities that can produce widespread societal benefits, such as:

Transparency: Free access to social media data allows researchers working on the perception of topical issues, such as the impact of sea level rising on coastal communities or that of social inequality on life expectancy, to gain insights into the expressed views, expectations, preferences, reactions and feelings of the people being (actually or potentially) affected by phenomena that are shaping our collective present, and will likely determine our future, as well as that of other species. Social media data access is also crucial for understanding the dynamics that push some to express their opinions and others to remain silent on issues that may directly affect them, such as climate change and social justice. Moreover, acknowledging the relevance of a particular issue for a wide spectrum of the population can be an incentive (and pressure) for representatives and governance bodies to activate, taking into consideration people’s expressed needs and concerns in relation to these topical issues. More generally, denying or limiting social media data access to researchers can damage evidence-based policy-making, for example with respect to issues related to climate change and its effects on citizens’ daily life.
Innovation: Open access to social media data fuels innovation in various sectors that build on top of academic and non-profit research, such as the AI industry. If researchers can’t access social media data they won't be able to develop new tools and models to analyze this data, which are widely employed, not only in academia. Furthermore, charging for data access could disproportionately damage and deter innovative research projects with unconventional or exploratory purposes related to social media and their data, which may not have guaranteed or clearly foreseeable outcomes or applications.
Accountability of social media platforms and users: Social media data can be used to hold platforms accountable for their impact on society, including issues like mis- and dis-information, trust erosion, and algorithmic biases. Restricting access to researchers could make it harder to observe, assess and address these issues.
Equal access and equal opportunities: Non-profit organizations and academic researchers with limited resources might struggle to afford social-media data access. This could further exacerbate knowledge and information inequalities, especially in poorer countries.
Knowledge consistency: If different social media platforms adopt different access policies, data sources might become inconsistent, making it difficult for researchers to compare them, or to analyze their dynamics and evolution across time, by integrating their findings across multiple platforms.
Civil society and community engagement: Access to social media data is crucial for NGOs, IGOs and other non-profit projects for understanding public discourse and socio-cultural movements. Without social media insight, civil society projects might struggle to engage citizens effectively in participatory deliberation or policy-making processes. Moreover, the ability to see how groups of citizens deliberate through social media is essential for facilitating understanding among groups. Lack of access to data could hinder efforts to bridge divides.
Accountability of elected bodies and representatives: Social media conversations provide citizen’s feedback on policies and their perceived effects. It is therefore important for holding representatives, governance bodies and institutions accountable and reactive. For example, if data access is limited potential abuses may go more easily unnoticed.

As you may have understood, open and free access to social media data for academic, non-profit, and civil society projects is essential for maintaining transparency and ensuring accountability in democratic countries. For this reason we must protect it, and you should help us in doing so.

The opinions expressed in this article are those of the author. They do not reflect the opinions or views of anyone else.

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.2

2023-10-06T09:09:50+00:00

Carlo R. M. A. Santagiustina, Venice International University.

-Continues from Part 1

Having worked in the last decade on several EU research projects, like MUHAI, which aim to study social phenomena, such as inequality perception, through user-generated data from social media, I feel the need to put you on alert. Social media, and the information they collect, are certainly formidable tools for studying and comprehending social phenomena, but, unfortunately, they are also formidable artifacts for attempting to influence and manipulate online crowds. Therefore, they should be considered critical (dual-use) technologies in relation to freedom of expression, but also in relation to social justice, peacekeeping and for the proper functioning of public institutions as well as of markets. This is especially relevant in liberal democracies that, from this point of view, are particularly vulnerable on both sides.

Social Media platforms can be considered the contemporary and digital equivalent of the agoras in ancient Greece, and just as in ancient agoras some people discussed public affairs while others did private business, the same occurs nowadays in these platforms. Both usages are per-se legitimate. But differently from what occurred in the past, nowadays citizens, representatives, institutions, and civil society have little, if no, control on how these private online agoras are employed, and on the scopes for which the data generated by citizens is sold to (and used by) third parties, for political, social or commercial exploitation. Social media companies are certainly required to comply with privacy and data protection regulation, which may partially protect users, as single individuals and organizations, but certainly this type of protection does not shield us as individuals belonging to, or identifying with groups or communities, based on life-styles, values, beliefs, preferences or any other form of shared identity or common behavior. Because, once user-data is aggregated and anonymized, it can be used for all sorts of purposes, from profiling, categorization and personalized advertising to political influencing campaigns targeting groups of users that are similar in terms of one or several aspects of their online behavior and identity. What is known about social media users at the aggregate level can hence be used to affect their views, preferences and behaviors. Since these platforms are populated by millions of citizens from dozens of countries, including totalitarian countries and façade democracies, many actors and organizations may have incentives to profit on top of their opacity, by using these platforms and their data for manipulating opinions, either of large and varied user populations, or, of carefully targeted influential audiences.

Virtual clashes roar, divergent views explore, data locked, voices sore.

Some social media platforms, like Twitter, have become for the aforementioned reasons virtual -but no less violent- battlegrounds. Where armies of users and bots driven by divergent interests and views, like pro-Russians and pro-Ukrainians, confront each other in harsh debates for setting the agenda of politicians and governance bodies or for influencing the opinions of consumers and citizens that use them. Other platforms, like Parler, stimulate by design the self-selection of aligned users and contents through homophily. Shared preferences, expectations and views that are sufficiently widespread among their communities, shield community insiders and their partisan convictions from the risks and wonders of communicating with people with differing opinions.

Unfortunately, in most cases, the growth of these private platforms and of the techniques for analyzing the rich and big data generated by their userbases, was followed by further restrictions for freely accessing the latter for non-profit research purposes. The recent diffusion and commercial success of some general-scope LLMs, which can also be trained using textual data from social media, has unfortunately exacerbated this process of user-data privatization and monetization. This is especially problematic for social science research and other non-profit projects, like AquaGranda: a Digital Community Memory, which, given their nature, do not (and cannot) aim to generate a profit from the usage of social-media data, and therefore have great difficulties in covering data access costs. This category is rather broad, and, besides researchers from academia and public research programs, it also includes researchers working for IGOs and NGOs, artists and activists, advocacy and volunteering groups and other non-profit organizations, who are also those whose activities and outcomes can possibly generate the greatest collective benefits and positive externalities for the public.

Short-circuiting non-profit research programs that study social, political, economic or socio-natural phenomena through the data generated by users on social media is easier than it may seem, and some platform managers and shareholders may want to do it simply to ask (data-) protection money also to academia, for continuing projects already underway.

Social media data policy changes are not simply due to privacy and data protection regulations and related concerns of the platforms that collect user-generated data. But rather, they often stem from the desire to further privatize and monetise the value of data contributed in exchange for no compensation by online communities, that is: your data. The data that you, your friends and your family, among others, generated by posting contents and commenting on each-others’ posts in the last two decades. Unfortunately, this process is not limited to Twitter, which recently withdrew free access to verified research projects, by deleting academic projects and their credentials from the Twitter developer portal without any notice. For example, also Reddit policies were recently changed, and this obliged the free PushShift archive to close its doors also to research projects. As strange as it may seem, Reddit moderators can still access the PushShift archive and its APIs but researchers from academia cannot. These are only two highly visible instances in a rapidly evolving social media landscape, which seems to want to make itself more and more profit-oriented and opaque.

At this point, a couple of concrete questions may be running through your mind:

What is lost when social media platforms change their free data access policies to researchers and other non-profit organizations?
What could occur if, because of the increasing data-access costs and constraints, civil society and citizen-science projects are denied the opportunity to see and comprehend how communities organize, debate and deliberate through social media?

(End of Chapter 2 – Find the answers in the next and last chapter)

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.1

2023-10-06T08:54:51+00:00

Carlo R. M. A. Santagiustina, Venice International University.

From climate change to inequalities: Exploring the societal value of social-media for understanding public concerns and the perceptions of anthropogenic phenomena at a time when user data is increasingly monetized.

Web weaves lives, spheres interlace, online's intimate embrace reshapes human space.

In the last two decades, the Web has become a multilayered architecture for distributed communication that connects more than 5 billion human beings. Online communication is nowadays ubiquitous and permeates all spheres of our daily activities: from family relations to work activities; from hobbies to political propaganda; from volunteering to advocacy. Online communication is not simply answering a functional role related to people's insatiable need for information or gossip, it can trigger, permeate and shape intimate spheres of our life, producing profound and widespread transformations at the individual and collective level, shaping our identities, understandings, beliefs and preferences.

The diffusion of smartphones and of social media apps, has rendered communication increasingly multimodal and multimedial. Through our online posting activity, social, cultural, political and economic phenomena can get easily intertwined and percolate from physical to virtual spaces. For example, they can flow from news platforms to social media, to markets and back again to the physical world, affecting its abundances and famines.

On the Web, what might seem volatile as, for example, an opinion about a natural event, like drought, or about a market phenomenon, like inflation, once published online starts acquiring a socio-cultural texture and physiognomy, by occupying and diffusing in a virtual space and by progressively acquiring a set of roles in relation to the online ecosystem in which it is uploaded. These online ecosystems, despite being virtual, are culturalized, socialized, politicized and commoditized, similarly to other environments in which humans operate. That is, they are endowed with morphologies characterizing the aforementioned dimensions of human systems, in which contents and interactions are embedded. Therefore, online posts on social media go far beyond the bits used to store their digitized information and multimedia contents, and are embedded into complex social, cultural, political and economic contexts, to which they are linked and with which they are entangled.

Opinions, preferences, and expectations, as well as expectations of others’ opinions, preferences and expectations, can heavily impact human systems and their institutions, such as markets or governance bodies, affecting their functioning, stability and outcomes. For example, in a few hours, a short video posted on snapchat capturing a clash between police and activists protesting against the inaction of their government in relation to rising inequalities caused by extreme weather events, will be observed, probably in real time, by multiple journalists and bloggers around the globe, some of which will rapidly publish polarizing or clickbait online articles on the subject, which ‌could give rise to the outrage of a broader section of the population that will read them, which will probably express their reactions, feelings and expectations through another cascade of posts and comments on their preferred platforms, which, if not rapidly addressed by competent and reactive institutions, could produce broader social tensions, both in the digital and in the physical space, potentially creating a conflict spiral.

Private platforms gain, Twitter's X-changes reign, Blue checks cost, trust is lost, shifts in the digital domain.

Most of the social media platforms, in which phenomena like the aforementioned spark and propagate, being private, aim to expand their profits and acquire new market quotas. Hence they operate for increasing actual and projected revenue streams. Recent changes of Twitter, now also referred to as X, teach us that, in a few months, online platforms can drastically change in terms of business models, social interaction architectures, transparency, data access policies and search-engine algorithms. These changes may in turn affect the views, incentives to communicate and interaction patterns of their userbases.

For example, among the many changes implemented by Twitter in recent months, one of the most significant is the decision to monetize the system of verified users, those with the blue checks in their profile, who now have also to pay a subscription for displaying this badge, which was previously a fair signal of the trustworthiness of Twitter sources, independently from users’ willingness and possibility to pay for it.

Among the many negative effects of this change, one, illustrated in this (https://www.washingtonpost.com/technology/2023/02/22/russian-propagandists-said-buy-twitter-blue-check-verifications) Washington Post’s article authored by Joseph Menn, is certainly a noteworthy example of the type of users that could be attracted by, and benefit the most from, these recent changes. The article’s title: “Russian propagandists are buying Twitter blue-check verifications”, speaks for itself.

Will this change, related to a new monetization strategy, destroy or bias the informative value of Twitter's blue check? If so, will the consumption of information generated by verified users change? and will Twitter users’ views be increasingly affected by “blue checked” partisan sources, which may want to afford the price of this subscription because of the benefits they derive from being labeled as verified sources? Is Twitter closing an eye, by allowing opaque profiles to display blue checks, only for a financial gain? Is this conflict of interest, due to Twitter’s incentive to maintain some opaque verified-user catchment areas, reshaping online debates in democratic countries?

Until mid-June 2023, with Twitter data freely accessible for academic research, a team of social scientists could have easily investigated these questions, to understand what is going on and to hence explain their findings to citizens, public institutions and civil society.

(End of Chapter 1 – What effects had theTwitter/X changes? Discover them in Chapter 2!)

A Digital Assistant for Scientific Discovery in the Social Sciences and Humanities

2022-11-14T13:13:08+00:00

Lise Stork, VUA.

Scientific discovery aims to explain the mechanisms that govern our world. In the social sciences and humanities, scientists are interested in our social world; societies and the individuals within them. They research, for instance, the mechanisms that cause social divides. Why do some groups die younger than others? Why do women earn less than men? How do the occupations of your (grand)parents influence your own career?

In the natural sciences, one talks about ‘theories’, ‘mechanisms’ and ‘laws’, where the emphasis lies on mathematical calculation, precise theories and experimental design of research studies. The same is not as trivial for (historical) social sciences, where researchers rely mainly on observational studies instead of experimental studies, and test research hypotheses in which variables are often social constructs: concepts that do not exist in our objective reality, but through interactions between humans, for example intelligence or nationality.

Both strands of science share that they want to further scientific discovery, and thus find it crucial to be precise and transparent about what is measured and what that means. Transparency is paramount for uptake and reuse of scientific output, which are necessary ingredients in the cycle of scientific discovery (see figure). Existing work lays the foundation for the generation of novel hypotheses. For the social sciences and humanities, where variables can be social constructs, such transparency is crucial as hypotheses might be tested with distinct constructs in the methods section. For example, how was social stratification–the ranking of people according to their occupation-measured? What classification was used to measure social class? What characteristics were measured to create a variable for well-being?

The scientific method. cc Efbrazil, CC BY-SA 4.0
via Wikimedia Commons

The complexity of these social constructs make surveying the literature for hypotheses, results and their specific measurements a cumbersome task. Natural language can be ambiguous or unclear, and therefore challenging to be interpreted precisely by humans as well as machines. After a literature search, specific details on what has been measured and why need to be rediscovered through precise reading, and related to what has been done in other studies. There is thus a need for the improvement of the digital infrastructure underlying scientific publication, to stimulate a deep understanding, and reuse of existing hypotheses, methods and findings.

Researchers in the MUHAI project together with domain experts from the International Institute of Social History (IISG) try to enhance the comparability of research outcomes. Inspired by the Semantic Web community’s agenda to make knowledge and data FAIR: findable, accessible, interoperable and reusable, these researchers aim to publish scientific hypotheses online as structured data. Specifically, work is being done on (i) the development of a semantic model for capturing social history hypotheses, and (ii) a digital assistant for authoring of social hypotheses as structured data, as well as the design of novel experiments. With such an application, we work towards a shared knowledge database, a shared memory of social science and humanities knowledge, that can be used in a variety of applications: from semantic search over the body of literature, to the automated discovery of novel insights and research hypotheses.

Credits
Intro image - Quarto Stato by Giuseppe Pellizza da Volpedo via Wikicommons

Narrativizing Knowledge Graphs

2022-10-18T13:54:20+00:00

Robert Porzel, UHB.

Any natural language expression of a set of facts — that can be represented as a knowledge graph — will more or less overtly assume a specific perspective on these facts. In this work we see the conversion of a given knowledge graph into natural language as the construction of a narrative about the assertions made by the knowledge graph. We, therefore, constructed a specific pipeline that can be applied to produce linguistic narratives from knowledge graphs using an ontological layer and corresponding rules that turn a knowledge graph into a semantic specification for natural language generation. Critically, narratives are seen as necessarily committing to specific perspectives taken on the facts presented. We show how this most commonly neglected facet of producing summaries of facts can be brought under control.
In the popular novel Stranger in a Strange Land Robert Heinlein introduces a cast of people who have been trained to speak only non-subjective truths containing neither valence nor assumptions. When describing a war-like situation it might, theoretically, be possible to say that, for example, some governmental head of a country gave an order to the army to move into another country by force. Natural language renditions of corresponding states of affairs usually contain expressions such as invading or liberating that assume a specific perspective (taking sides) and denote some valuation of the situation at hand. In other words, rather than objective and neutral truth-sayers, we are
spinning narratives out of the facts on the ground.

The concept of a narrative has migrated from its original domain in the literary sciences to a multitude of diverse and increasingly distant research fields. It has become an important element in research on games or in history } to name a few of these domains. At long last it has also arrived in the cognitive sciences where narratives are regarded to be a central means of sense making . From there it was merely a short jump over to the field of computer science where the concept is employed to describe semantically annotated episodes of recorded activities and has, subsequently,
been formalized by the MUHAI project using description logics. When we make assertions about people or events to capture them in a knowledge graph, we accumulate information that is supposed to represent the ground truth. In narratology this is often called the fabula. This fabula can represent episodes, e.g.:

for logging an autonomous robot the fabula can represent trajectories of body parts and activity-specific force events.
for modeling historical or current events, as done by the EventKG knowledge base, the fabula usually consists of events and their participants.

While these fabulae contain large quantities of information they are, by themselves, not very meaningful. Only when we put them into a pragmatic context we assign additional meaning to them. For example, we can interpret the same observed episode as either throwing something or dropping something. This difference in the narrativization, consequently, yields two distinct narratives:
1. Sherlock dropped the glass onto the floor
2. Sherlock threw the glass onto the floor

It is important to note that the knowledge graph representing these two minimal narratives can be identical. We consequently differentiate between a factual knowledge graph, i.e. the fabula, which has not been narrativized and a (language-based) description of it, i.e. the narrative. This pairs a situation with a selected conceptualization, i.e. interpretation, thereof and renders the latter in natural language. In addition to becoming meaningful, the description will, in turn, evoke a pragmatic stance that ascribes, for example, a specific perspective and intention to the agent(s) acting in specific roles within the narrative. The more general contribution of this work is to examine
certain elements of narrative mechanics, as part of a larger effort of the MUHAI project to understand the mechanics of conflictual narratives. Specifically, we provide a technological scaffolding for the process of constructing such narratives in order to further empirical research on how narratives emerge in the wild. We have, therefore, constructed a system that takes a knowledge graph, i.e. a fabula, as input and converts it to a narrative.

This work is by no means done and, therefore, still in progress. Along with assuming a specific perspective on an episode, narratives also feature a teleological stance and in many cases also a normative valence. This needs to be included to arrive at a comprehensive model that allows for reasoning about narratives, e.g. what the specific differences between two distinct narrativizations of an identical episode are and even what they mean. This type of reasoning would extend the semantics employed, for example, in opinion mining and sentiment analysis, as narratives could then be grouped and compared in terms of perspective, stance or valence.

The contribution of this work is to provide a representational framework that can readily be employed in cognitive robotics to counterpart the notion of a task that is given to a robotic agent and can be executed by finding an appropriate action with the notion of a narrative that looks at an action and seeks to makes sense of it. Ideally, one could match the task leading to an action and the narrative describing the action to express if that task has been successfully executed by the agent from the point of view of the narrativizer. As most readers will know there can be vast differences in these judgments, for example, between parents and children concerning the question if a room has been properly cleaned.

Again, it is important to note that this approach explicitly rejects an objective notion of a narrative, i.e. to equate a narrative with what has objectively happened and can be recorded an stored as data. As a descriptive notion a narrative assumes a specific point of view on the episodic event. It, therefore, provides a spin on the event and is subjective. Nevertheless, these views can be shared by collectives and become well established frames in which larger historical or everyday episodic events can be seen by societies or groups.

Input

Ontological Representation

Output

Credits
Intro image by NASA/JPL-Caltech

Economists’ inequality narratives (on Twitter) before and after the COVID-19 outbreak

2021-12-16T14:37:14+00:00

Carlo R. M. A. Santagiustina, VIU.

Facilitating citizens’ and scholars’ understanding of social inequality narratives is one of the end objectives of MUHAI.

Figure 1: Investigating social inequality (from the MUHAI perspective)

Inequality-related narratives can be created, circulated and employed at two distinct but intertwined debate levels. The first one is made by scholarly and scientific debates, which are mostly carried out by academic researchers and field experts, specialised in the measurement, analysis and modeling of specific types of inequalities, like inequalities of access to health and care services [1]. Experts’ narratives can, for example, be captured through the analysis of peer-reviewed literature, and of related reports and statistics, like those about between-country inequality of access to COVID-19 vaccines. The second level includes societal and popular debates, which increasingly reside on online social media and news platforms, and possibly involve millions of participants, mostly non-specialists. Societal debates can take place on multiple platforms at the same time. These debates and their narratives often focus on the perceived causes and effects of salient events, of policies, and issues creating collective interest or concern, like the COVID-19 pandemic and its consequences on inflation, employment or workers’ productivity.

Figure 2: The two levels of the social inequality debate (and their intersection), from [2]

While scholarly debates tend to be technical, formal, subject specific, evidence based, and involve relatively small numbers of knowledgeable participants, societal debates are generally constructed around anecdotal evidence, emotionally salient breaking news, personal experiences, (not necessarily justified) personal worldviews, and ideological positions.

Despite being permeated by subjectivity, partisanship and irrationality, societal debates are very important if one wants to understand inequality narratives, as well as their political use and socio-economic impact. In fact, it is through societal debates that a large spectrum of the civil society gathers information, forms (potentially biased) mental-representations of inequality-related issues, and then, on the basis of the latter, take or justify (potentially suboptimal) decisions.

Participants to societal debates about inequality range from scholars and experts to elected officials, public and private governance bodies, NGOs, activists, and citizens. Among all participants, scholars and experts are the only group taking part in both levels of the debate, and having to base their narratives on justified and explainable representations.

Given the complexity of the debate on social inequalities, to start reconstructing this puzzle, we have focused on economists’ posts published on Twitter. Scholars’ tweets about inequality are an ideal case-study for our investigation, because they allow to explore which inequality-related issues are discussed and spread by them across the Web, which data and news sources they mentioned, how their contents are framed for web audiences, and which of their narratives capture the general public’s attention.

In particular, by looking at economists' Twitter posts we can identify and explore the information sources and argumentation strategies used by specialists. One can also map how inequality interpretation frames ripple from academic literature to social media, possibly affecting the public’s understanding and perceptions of inequality-related issues.

Figure 3: Visual summary of the determinants of the perception of inequality, from [2]

To unveil how inequality narratives are argued in relation to their social(isation) contexts, we analyse 42131 twitter posts written in English explicitly referring to inequality, published by 1428 economists registered in the RePEc online repository (https://ideas.repec.org/i/etwitter.html ).

Tweets in this dataset can be explored by entering (on Twitter) the query (or URL) here below:
https://twitter.com/search?q=list%3Arepec_signup%2FEconomists%20%20(inequality%20OR%20inequalities)

Figure 4 summarises the workflow of the proposed analysis of inequality-related topics, which is based on a seeded Latent Dirichlet Allocation (LDA) model enriched, through the LDA2Net methodology[4], with word dependency relations (weighted using TF-IDF).

Figure 4: Visual summary of the workflow.

Differently from (more) technical aspects of the policy debate, inequality-related issues discussed in tweets tend to fuel the interest and concerns of large audiences: on average, each tweet about inequality posted by a RePEc scholar is retweeted nine times, and quoted or replied to by two other tweets.

As shown in Figure 5, which displays the top named entities and URL domain (by category), a great variety of generalist and specialised news sources are mentioned in tweets about inequality.

Figure 5: Wordcloud of (top 200) most mentioned named entities and URL domains (by category).

As expected, frequently mentioned Places include English speaking countries, like the U.S., U.K. and India, but also, other (non-English speaking) developing countries like Brazil and China, which clearly emerge among the top ranked Places in tweets about inequality. For what concerns Persons mentions, the wordcloud contains a mix of names referring to political figures, like Biden and Obama, and renowned economists working in the field of social or economic inequality, like Piketty, Deaton, Atkinson, Stiglitz, among others, with the latter group being relatively more relevant than the former one. This suggests that the online debate about inequality heavily focuses on academic works and (divulgative) books on related issues. Finally, the wordcloud of most mentioned Organizations shows that, besides media outlets, also the policy recommendations of international organizations, like the IMF, the WorldBank, are often discussed in scholars’ posts related to inequality.

It is well known that the public perception of inequalities is closely related to salient events and rapidly evolving themes capable of generating social concerns. Therefore, by analysing economists’ attention (across time) to different topics (i.e., types of inequalities), we can understand which issues are trending, and which ones are relatively neglected, with respect to others, both at the worldwide scale or in specific countries. These dynamics are summarised in Figure 6, which shows the evolution of RePEc economists’ attention to the different types of inequality.

Figure 6: Average topic propensities in tweets, by year and by topic (i.e, inequality type)

As Figure 6 shows, the attention towards different types of inequalities varies a lot from year to year. In particular, during the last two years (2020-2021) we can clearly notice the effect of the COVID-19 pandemic on the attention to health inequality, which jumped from the third last position to the first position, in only one year. This example suggests that scholars’ debate about inequalities is reactive to exogenous events, like a pandemic, which may rapidly reshape inequality figures and their perceptions, at the worldwide scale.

Figure 7 represents, respectively, the topic-specific narratives networks for the Health inequality (top figure) and Education inequality (bottom figure) topics in the years 2019 and 2020, this, to capture possible changes in the narratives related to these types of inequalities, occurring the year of the COVID-19 outbreak.

Figure 7: Topic-specific word relations networks for Health Inequality and Education Inequality, the networks were filtered keeping only top 50 edges (i.e., word-relations) by TF-IDF weight. In yellow: topic-specific seed words.

By comparing the two Health inequality networks, we observe that new words, and word relations related to COVID-19 emerge among the top 50 edges in 2020, like the relation between disease and exacerbate, referring to how the COVID-19 disease exacerbates existing health inequalities. Similarly, we notice that the relation between school and closure emerges in the top 50 relations network (year 2020) for the Education inequality topic. Highlighting the relevance of narratives about the effect of school closures in discussions about education inequalities.

While limited in terms of size of the dataset and applied methods, this preliminary analysis allows us to partially unveil the enormous potential offered by social media data, which will be used by MUHAI to understand more in depth scholars’ narratives and debates about inequalities.

References:
[1] Topriceanu, C.C., Wong, A., Moon, J.C., Hughes, A.D., Bann, D., Chaturvedi, N., Patalay, P., Conti, G. and Captur, G., 2021. Evaluating access to health and care services during lockdown by the COVID-19 survey in five UK national longitudinal studies. BMJ open, 11(3), p.e045813.
[2] Santagiustina C.R.M.A., 2021. How RePEc economists conceptualise and discuss inequalities on Twitter. Working Paper.
[3] Santagiustina C.R.M.A., 2021. From Narrative Economics to Economists' Narratives. MUHAI Cognitive Foundations deliverable (forthcoming).
[4] Minello G., Santagiustina C.R.M.A., Warglien M., 2021. LDA2Net: Digging under the surface of COVID-19 topics in scientific literature. Arxiv preprint arXiv:2112.01181v.

* This article contains excerpts and figures from the preprint versions of the following works:
- Santagiustina C.R.M.A., 2021. How RePEc economists conceptualise and discuss inequalities on Twitter. Working Paper.
- Santagiustina C.R.M.A., 2021. From Narrative Economics to Economists' Narratives. MUHAI Cognitive Foundations deliverable (forthcoming)

Making sense of events within a story

2021-11-17T14:01:41+00:00

Inès Blin, CSL.

Opening of the Estates-General in Versailles 5 May 1789. It was a general assembly gathering the three estates at the time: the clergy (First), the nobility (Second) and the commoners (Third). Reference of the image here.

Introduction
We are constantly building a posteriori stories about how events happened, and about how you can make connections to come up with a coherent whole. To some extent, it is an extension of the Five W's - Who? What? When? Where? Why? -, the five questions that are considered the most basic in problem solving.

The MUHAI project aims to push current AI towards more meaning and understanding. On one hand, meanings are distinctions (entities and categories) used to describe the events, actors, and entities involved in a specific event. On the other hand, understanding consists in combining those meanings to build coherent narratives.

Whereas the story events are numbered chronologically, the plot events are connected by cause-and-effect relationships. Reference of the image here.

Within this context, one first step towards building narrative networks is to decompose a bigger event into smaller events, and find the relevant entities for each of those events. Related to the picture above, that would be collecting the story events.

Knowledge graphs can be leveraged to realise such a goal. In this data structure, nodes represent real-world entities such as dated events and people. Edges between those nodes indicate their relationships, e.g. Maximilien Robespierre → participant in → French Revolution. The aim of knowledge graphs is therefore to go beyond the simple form of a word, and associate each entity with relevant properties and attributes.

Use Case: the French Revolution
One example of a free and open knowledge graph is Wikidata, which contains around 100,000,000 data items.

One way to build narrative networks is to assess the coherence between the textual description of an event and its closely linked nodes and edges in a knowledge graph. In Wikidata, the French Revolution is described as a social and political revolution in France. Thus how relevant is this description, given the neighborhood of the French Revolution node in Wikidata?

By analysing edges related to the revolution node, a human can map a revolution to regime change and social interaction, and can therefore understand how it relates to the social and political domain. Even if the task is simple for a human, it is much more complex for a machine, who initially has no grounded meaning - pointers to ‘the real world’. An intelligent system should be able to reason on why a description of the French Revolution is relevant, given the data structure.

Another way to build narrative networks is to collect sub events related to the event, and understand how they are connected. For instance, one could manually follow links in Wikidata in order to identify events related to the French Revolution. 3 different paths from events to the French Revolution were chosen to retrieve such events, for over 45 events. How can you then connect the dots and understand how events are interconnected?

Analysing the outcome of each event helped doing so. For instance, the Insurrection of 31 May - 2 June marked the transition between the Girondin Convention and the Montagnard Convention during the French First Republic, whereas the Coup of 9 Thermidor marked the transition between the Montagnard Convention and the Thermidorian one. Likewise, the Coup of 18 Brumaire ended the French Directory and started the French Consulate.

As a conclusion, the aim of this case study on the French Revolution was to show how semantic web technologies like knowledge graphs can be leveraged to build narrative networks. Two such examples were further analysed: assessing the coherence of a text content and a graph structure, and understanding how events are connected.

Further References
● Meghini, C., Bartalesi, V., & Metilli, D. (2020). Representing Narratives in Digital Libraries: The Narrative Ontology. Semantic Web Journal, 19.
● Wikidata node of the French Revolution

Talking (online) about inequality: Towards an observatory on inequality narratives

2021-05-10T13:37:16+00:00

Carlo R. M. A. Santagiustina.

“Storytelling is a means by which representatives of new communities may introduce their views into the dialogue about the way society should be governed. Stories offer insights into the particulars of lives lived at the margins of society. This is not just true of our times. In Biblical history, storytellers for oppressed groups told tales of hope and struggle. Other storytellers have directed their attention to the oppressors, reminding them of the day when they would be called to account. Stories thus perform multiple functions, allowing us to uncover a more layered reality than is immediately apparent.”
Bell D. (1999)

Part of the MUHAI objectives is developing tools to help humans understand media materials, such as tweets or articles, on critical social issues, in particular socio-economic inequality.
In the last centuries, philosophers and social scientists have conceptualized and measured socio-economic inequalities in multiple ways. Each of the proposed paradigms corresponds to a specific identification, categorization and commensuration of inequalities, as well as an interpretative frame that offers a particular perspective on inequality phenomena of a particular kind, in a specific reference population and time.

Each paradigm proposed by contemporary sociologists or economists also comes with its methods, which can be quantitative or qualitative, descriptive or normative, policy-oriented or not. Also, the measures of inequality that researchers have created and used are not independent from the reasons for building them first, which is identifying specific types of inequalities for debating and intervening on them (Simon et al., 2015). In this respect, Strathern M. (1997) showed that when “a measure becomes a target, it ceases to be a good measure”, is this true also for inequality measures?
Certainly, besides being used for grounding inequality debates on “hard numbers”, relative and absolute inequality measures, such as the Lorenz curve or the Gini index, are nowadays also used as evidence for building arguments in favour or against specific inequality reduction policies (Alesina et al., 2016). For example, they may be used as motivations and justifications for prioritising, in the political agenda of a government, the reduction of specific forms of inequalities (e.g., ethnic inequality), with respect to others (e.g., generational inequality).

Moreover, despite the objective benefits offered by studies based on sound statistics, inequality measures and synthetic statistics tend to focus the attention of policymakers and citizens on measurable inequalities, which -by construction- are often unable to capture and appraise the full extent to which multiform inequalities, such as gender inequality, permeate and affect wealth distribution and working opportunities through intricate -and often tacit- socio-cultural norms.

Just as inequality emerges from a comparison between groups on specific dimensions, so its statistics are used to compare one or more countries or populations, for example, to comfort a population by explaining that inequality of a particular kind is being progressively reduced or is lower than in a neighbouring country, or to push a group that sees itself as disadvantaged by a statistical description of the present situation, to take action and to demand further policy and legislative measures to reduce an identified gap.

Inequality narratives, also the ones based on statistics and quantitative models have the power to affect inequality perceptions and can hence be used to frame and shape inequality debates. Therefore, inequality representations are never neutral in terms of their effect on opinions and policy demand for reducing -or not - inequalities.
If on one hand, many studies have focused on the opinion of the experts and policymakers, on the other hand, relatively little work has been done on how the general population perceives inequalities, debates about them and activates to reduce them (Morrison et al., 2015; Amiel & Cowell, 1999).
The complex interaction between inequality research and its outputs (reports, statistics, articles), collective inequality perceptions and narratives, and inequality policies make it a unique field of study for an AI project that focuses on understanding human narratives and argumentation dynamics. Thanks to the integration of inequality information from reports, statistical data, and social media, like Twitter, the MUHAI project will take the first steps towards the mapping of the European inequality debate and its narratives. This, to see which opinions and narrative strategies, concerning specific types of inequality, have attracted the attention of the media, citizens and policymakers, at a specific time.

Moreover, the coronavirus pandemic has raised a new critical issue: the need to address the systemic relationship between risks distribution and inequality. Inequalities in the distribution of specific risks (e.g., Covid-19 health risks and vaccination risks), being based on expected rather than revealed differences, are particularly difficult to identify, commensurate and mitigate with respect to more “visible” forms of inequality (e.g., income and health inequality). As a result, EU citizens and policymakers are potentially more easily prone to biases and misinterpretations when having to understand and debate about the former inequalities. Therefore, by capturing, interpreting and mapping online inequality narratives, MUHAI’s observatory will also be able to highlight in which terms different types of inequalities are (mis)perceived and socially amplified by EU media and citizens.

References:
Coleman, J. S. (1974). Inequality, sociology, and moral philosophy. American Journal of Sociology, 80(3), 739-764.
Strathern, M. (1997). ‘Improving ratings’: audit in the British University system. European review, 5(3), 305-321.
Bell, D. (1999). The power of narrative. Legal Stud. F., 23, 315.
Morrison, J., Pons-Vigués, M., Díez, E., Pasarin, M. I., Salas-Nicás, S., & Borrell, C. (2015). Perceptions and beliefs of public policymakers in a Southern European city. International journal for equity in health, 14(1), 1-10.
Amiel, Y., & Cowell, F. (1999). Thinking about inequality: Personal judgment and income distributions. Cambridge University Press.
Simon, P., Piché, V., & Gagnon, A. A. (2015). Social statistics and ethnic diversity: cross-national perspectives in classifications and identity politics (p. 244). Springer Nature.
Alesina, A., Michalopoulos, S., & Papaioannou, E. (2016). Ethnic inequality. Journal of Political Economy, 124(2), 428-488.

Understanding Society

2021-03-24T16:05:06+00:00

Lise Stork, VUA.

Why are the neighbourhoods in some cities sharply divided along income boundaries, while in other cities not? Was this always the case in different periods of history? And in different cultures? Has social mobility increased or decreased over time? Why does life expectancy correlate with income?

Disparities in income and opportunity for personal development are continuous sources of frustration and social divide. The deeply unequal global landscape of modern society increases the importance of studies into the origins and persistence of inequality. To this end, the MUHAI project aims to develop a technological infrastructure to aid social scientists with the generation and explanation of research hypotheses. In building such a “social observatory” the emphasis lies on cooperation between human and system, where capabilities of both complement one another.

Social scientists commonly search for indicators that contribute to or cause the origins or persistence of inequality between social groups. They do this by taking a close look at data that describe the results of societal mechanisms, such as the division of labour and income. At the Dutch International Institute of Social History (IISH) for instance, social scientists investigate the global development of labour and labour relations. For this purpose, they collect, process and link historical archives such as handwritten census, accounts of the history of municipalities, registers of births, marriages and deaths, tax surveys, and historical maps.

Research into historical datasets is challenging on multiple levels. For example: the meaning of occupations and other concepts change over time, datasets often contain biases whenever data are collected in specific regions or among certain social groups (for instance only among those that earn more than the marginal income), and although it is possible to detect certain trends computationally, finding an understandable explanation for the cause of such trends seems an insurmountable task.

Knowledge graphs are interconnected networks of data that represent historical facts and knowledge about social phenomena and everyday life. In such graphs, nodes represent real-world entities such as events, locations, or individuals of a population, and edges indicate their relationships with other entities, such as the age or birthplace of a specific individual, e.g., person → livesIn → Paris. Researchers from the IISH routinely analyse these graphs to discover patterns and find explanations for social phenomena such as socio-economic inequality. Using different techniques that will range from statistical techniques such as deep neural networks, to symbolic techniques such as automated reasoning, we will aid them with this process by discovering new knowledge, detecting clusters or trends, and most importantly, by formulating sensible causal explanations of such clusters and trends. A typical example would be the question why in 1814, the marriage numbers in France were double that of the years before and after? The explanation for this is that Napoleon issued a law, requiring all men who were unmarried by 1815 to join the army, so a lot of marriages were hastily arranged in 1814!. Our ambition is to develop human-centered AI techniques that can uncover such explanations by working in collaboration with social scientists.

So, rather than simply uncovering statistical patterns, we aim at creating a social observatory that provides social scientists with human understandable explanations of trends, such that scientists can turn these explanations into testable hypotheses, and obtain a deeper understanding of the value of certain hypotheses and potential sources of reasoning errors, such as selection bias and missing information. The aim of this observatory is therefore not to replace human capabilities, but to enhance them, with the MUHAI tools working in collaboration with them. Through the creation of understandable narratives of social inequality, researchers can improve and accelerate their research and paint a picture of general societal processes that cause long-standing societal inequality.

The Florentine Catasto of 1427

The Catasto is a tax assessment of the inhabitants of Florence and its surrounding territories between 1427 and 1429. In the Catasto, officials listed the wealth, debts, and assets of households in the Republic of Florence. Unlike many premodern tax assessments - which only taxed the rich - the Catasto aimed to include all households within the Republic. The Catasto allows to examine the relation between household size and wealth. In the city of Florence this relation was quite positive:

How can this correlation be explained? Were these households large because they could afford it, or were poor households also large because many family members were living under one roof?

Source: https://stories.datalegend.net/catasto/

blog - MUHAI

Study without ChatGPT… to work more wisely with AI

Paul Van Eecke (VUB)Katrien Beuls (UNamur)Tim Brys (VUB)Adapted by Folco Soffietti (VIU)

Source: Pexels, modified by Folco Soffietti

Adapted from the article “Studeren zonder ChatGPT, daarna verstandiger werken met AI” published on the Knack on the 9th of April 2024.

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.3

Carlo R. M. A. Santagiustina, Venice International University.

-Continues from Part 2

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.2

Carlo R. M. A. Santagiustina, Venice International University.

-Continues from Part 1

From digital archives to online observatories, the peaks and chasms of social-media based research Pt.1

Carlo R. M. A. Santagiustina, Venice International University.

A Digital Assistant for Scientific Discovery in the Social Sciences and Humanities

Lise Stork, VUA.

Narrativizing Knowledge Graphs

Robert Porzel, UHB.

Economists’ inequality narratives (on Twitter) before and after the COVID-19 outbreak

Carlo R. M. A. Santagiustina, VIU.

Making sense of events within a story

Inès Blin, CSL.

Talking (online) about inequality: Towards an observatory on inequality narratives

Carlo R. M. A. Santagiustina.

Understanding Society

Lise Stork, VUA.

The Florentine Catasto of 1427

Paul Van Eecke (VUB)
Katrien Beuls (UNamur)
Tim Brys (VUB)
Adapted by Folco Soffietti (VIU)