Antonella Ianni

Welcome to my webpage!

I am an Associate Professor in Economics at the University of Southampton (U.K.).

I do research mostly in the area of Microeconomic Theory and, more recently, also in Complexity Theory, Network Theory, Social Network Data and their use in Public Health.

I am the Head of PGT Programmes and I am in charge of the MSc in Economics, the MSc in Finance and Economics, the MSc in Finance and Econometrics and the MRes in Economics.

University of Southampton

Recent research

non-communicable diseases over social networks

(Interdisciplinary research)

This project aims at repositioning questions related to environment, behavior and public health to the domain of social network platforms, such as Twitter, by emphasizing the social, rather than just clinical, dimension of life-style related illnesses. 

This is joint work with M. Brede, E. Mentzakis and T. Wang and has received funding from IfLS, the School of Social Sciences, the Web Science Institute and the ESRC-DTC.

  • Structures and dynamics of multiplex social networks in online eating disorder community (2018)

User-generated data on social media provides high-resolution records of people' feelings, thoughts and behaviors to understand complex mental disorders. Prior studies either focus on content analysis without considering relational interactions between individuals, or ignore the multiplex and dynamic nature of social interactions. Here, we explore the multiplexity and the dynamics of interactions in online health communities through a large set of Twitter conversations between individuals who self-identified as eating disordered. By modeling interpersonal communication on different types of content through a multilayer network, we show that (i) different types of interactions have distinct network structures, e.g., interactions on private content tend to take place within small groups, and (ii) users play a different role in different types of interactions, e.g., hubs in exchanging pro-recovery content are less likely to be hubs in exchanging anti-recovery content. By measuring temporal characteristics of multilayer networks built based on users' conversations in different time periods, we further find that (i) the diversity of users' interests in different types of interactions decreases over time, and (ii) anti-recovery communities have a smaller number of hardcore members than other communities. Our findings shed light on the organization and evolution of an online health community. 

  • Social Interactions in Online Eating Disorder Communities: A Network Perspective (2018)
Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online. Based on a large set of tweets posted by individuals who self-identify with eating disorders online, we establish the existence of two communities: a large community reinforcing disordered eating behaviours and a second, smaller community supporting efforts to recover from the disease. We find that individuals tend to mainly interact with others within the same community, with limited interactions across communities and inter-community interactions characterized by more negative emotions than intra-community interactions. Moreover, by studying the associations between individuals' behavioural characteristics and interpersonal connections in the communication network, we present the first large-scale investigation of social norms in online health communities, particularly on how a community approves of individuals' behaviours. Our findings shed new light on how people form online health communities and can have broad clinical implications on disease prevention and online intervention.

  • Estimating Determinants of Attrition in Online Eating Disorder Community: An Instrumental Variables Approach (2018)
The use of social media as key health-information source has increased steadily among people affected by eating disorders. Intensive research has examined characteristics of individuals engaging in online communities, while little is known about discontinuation of engagement and the phenomenon of participants dropping out of these communities. This study aims to investigate characteristics of dropout behaviors among eating disordered individuals on Twitter and to estimate the causal effects of personal emotions and social networks on dropout behaviors. Using a snowball sampling method, we collected a set of individuals who self-identified with eating disorders in their Twitter profile descriptions, as well as their tweets and social networks, leading to 241,243,043 tweets from 208,063 users. Individuals' emotions are measured from their language use in tweets using an automatic sentiment analysis tool, and network centralities are measured from users' following networks. Dropout statuses of users are observed in a follow-up period 1.5 years later (from Feb. 11, 2016 to Aug. 17, 2017). Linear and survival regression instrumental variables models are used to estimate the effects of emotions and network centrality on dropout behaviors. An individual's attributes are instrumented with the attributes of the individual's followees (i.e., people who are followed by the individual). Eating disordered users have relatively short periods of activity on Twitter, with one half of our sample dropping out at 6 months after account creation. Active users show more negative emotions and higher network centralities than dropped-out users. Active users tend to connect to other active users, while dropped-out users tend to cluster together.

Eating disorders are complex mental disorders nd resposible for the highest mortlity rate among mental illnesses. Recent studies reveal taht user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studyig communities of people who discuss eatig disorders on social media, while few studies have explored communicty structures and interactions among individuals who suffer from these diseases over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connection with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioural patters and psychometric properties; 2. buyilding predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of soical connections beween eating disordered individuals on Twitter, we present the first homophily study among these communities on social medai. Our findings shed new light on how an eating disordered community develops over social media.

Since 2015

On voting and electoral campaigns: competing over voters' attention

(Microeconomics and experimental economics)

This project looks at various aspects of public opinion formation, voting behaviour and campaign spending in a way that incorporates recent behavioural findings into otherwise standard models.

Part of this work is joint with H. Marreiros and has received funding from the School of Social Sciences.

  • On the Heresthetics of Salience: Competing over Voters' Attention
We study a voting model in which two candidates compete for the attention of voters, who value both the spatial dimension of policy, as well as each candidate's personal attribute of valence. Candidates run in a winner-take-all election and draw voters' attention towards the attribute in which they enjoy a comparative advantage, by thus making it salient in voters' mind. The paper offers three contributions. First, it provides novel and significant experimental evidence in support of salient behaviour in voting. Second, it characterizes policy salient political equilibria as well as valence salient political equilibria with salient voters. Third, it suggests ways in which the notion of salience can be made operational, leading the way to testable implications. Experimental as well as theoretical results show that the median voter paradigm and its implications are challenged if voters are salient, as this raises a modeled attention externality, whereby strategic positioning of candidates in the policy dimension affects how attributes are perceived and ballots are cast.

  • Social Learning in a Referendum
We study a probabilistic voting model in which one, out of two, options is to be chosen by voters in a referendum. Voters may be informed or uninformed. Informed voters vote rationally on the basis of an informative private signal received by experts. Uninformed voters decide on the basis of the observation of their neighbours’ choices, via a process of social learning. We show that the entailed process of social learning accounts for an explicit dynamic spatial externality that leads to bandwagon in the dynamics of voters’ choices and aggregate public opinion. Results show that, no matter how precise the signal is, the mere presence of uninformed voters may lead to grossly inadequate outcomes. As we are able to characterize explicitly the unique limit distribution, we report on simulations and comparative statics results.

  • Social Learning and Electoral Campaigns 
We study a probabilistic voting model in which candidates compete for the attention of voters, who value both their ideology and their valence. While ideology is perfectly observable, valence is unknown to voters and estimated on the basis of some private signal, as well as on the observation of their neighbours' choices. We show that the entailed process of social learning whereby voters learn about candidates' valence accounts for an explicit dynamic spatial externality that leads to bandwagon in the dynamics of voters choices and aggregate public opinion. Results show that, unlike in a static set-up, strategic positioning of candidates may not be sufficient to internalize this externality and campaign funding should be directed to marginal voters, to guarantee that clusters of supportive voters are not exposed to sudden opinion swings.

On Learning - on and off networks


This projects studies different learning models, where individuals learn from their history (through reinforcement) or by observing the action taken by their peers (through social learning on a network).

Part of this work is joint with A. Guarino and has received funding from the ESRC, from the EUI and from the Ca Foscari University of Venice.

We study social learning in a large population of agents who only observe the actions taken by their neighbours. Agents have to choose one, out of two, reversible actions, each optimal in one, out of two, unknown states of the world. Each agent chooses rationally, on the basis of private information and of the observation of his neighbours’ actions. Agents can repeatedly update their choices at revision opportunities that they receive in a random sequential order. We show that if agents receive equally informative signals and observe both neighbours, then actions converge exponentially fast to a configuration where some agents are permanently wrong. In contrast, if agents are unequally informed (in that some agents receive a perfectly informative signal and others are uninformed) and observe one neighbour only, then everyone will eventually choose the correct action. Convergence, however, obtains very slowly, at rate √t.

This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al. (2001). The main results of the paper show that, if the solution trajectories of the underlying replicator equation converge exponentially fast, then, with probability arbitrarily close to one, all the pathwise realizations of the reinforcement learning process will, from some time on, lie within an  band of that solution. The paper improves upon results currently available in the literature by showing that a reinforcement learning process that has been running for some time and is found sufficiently close to a strict Nash equilibrium, will reach it with probability one.

On Agent Based Modeling

(MICROECONOMICs, Computer Science)

This project deploys ABM to understand endogenous merging decisions in markets and the origins of money as a medium of exchange.

Part of this work is joing with C. Zedan, T. Moran, M. Brede, S. Bullock and J. Noble and has received funding from the DTC- Complexity.

We present an agent-based model of endogenous merger formation in a market with turnover of market participants. We describe the dynamics of the model and identify the conditions under which market competition is sufficiently disrupted to prompt extended periods during which mergers are desirable. We also demonstrate how merger waves can be triggered by industry shocks and firm overconfidence.

The benefits of money as a medium of exchange are obvious, but the historical origin of money is less clear. An existing economic model of monetary search is reproduced as an agent based simulation and an evolutionary algorithm is used to model social learning. This approach captures the way in which different equilibria can arise, including solutions in which one or two goods come to be used as money. In the case where monetary goods have identical properties, multiple equilibria can be reached with a dependence on the starting beliefs of agents. In our analysis we also consider the evolutionary dynamics that allow for a small chance of mutations in strategies. In some cases our findings show evolutionary paths by which use of particular monetary goods can collapse.


All teaching material is available via Blackboard.

Since 2015


I currently serve as the PGT Director for the following Programmes



Here is a 2-pages version of my cv.