Change My Mind: Data Driven Estimate of Open-Mindedness from Political Discussions

Pansanella, Valentina; Morini, Virginia; Squartini, Tiziano; Rossetti, Giulio

doi:10.1007/978-3-031-21127-0_8

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…This is of course an open and difficult problem: a practical method would be to analyze historical positions of each individual on other topics (for example, in the context of misinformation [ 12 ], the attached bias may correspond to the frequency at which conspiracy theories has been preferred to mainstream news in the past, by the individual). Another approach would be to first identify communities in a social network of interactions and then infer the average preferences of the individuals of the communities by analyzing the opinion uploads during internal and external interactions (similarly to [ 48 ]).…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Filippo

Contucci

Kertész

2023

Entropy

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Two of the main factors shaping an individual’s opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases’ strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Filippo

Contucci

Kertész

2023

Entropy

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“…To address this, we propose extending the current model to incorporate a baseline opinion that consistently influences the user in that direction. Finally, as we saw in 48 , real populations may have heterogeneous (opinion-dependent) levels of "open-mindedness", which could be taken into account to specify agents' peculiarities better, as well as heterogeneous activity levels as in 49 . Similarly to "open-mindedness" and activity levels, we plan to augment the current model with data-driven insights on media bias and user interactions with mass media and authoritative voices via online social networks.…”

Section: /13mentioning

confidence: 98%

Mass Media Impact on Opinion Evolution in Biased Digital Environments: a Bounded Confidence Model

Pansanella

Sîrbu

Kertész

et al. 2023

Preprint

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People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users' biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.

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“…For example, we know that individuals with radical opinions tend to be more stubborn [5,16]. Further, we know that the level of stubbornness may vary across ideological groups [9]. An important observation is that younger individuals are considered to be more vulnerable to social influence [23].…”

Section: Literaturementioning

confidence: 99%

“…Over the past few years, the situation has slowly changed [5][6][7][8][9][10]. Partially, this becomes possible due to the large amount of open data available from online resources.…”

Section: Introductionmentioning

confidence: 99%

“…An extremely important issue in studies of social influence and information dissemination is to understand how influential a particular individual is (that is, how effectively they influence other people) and how strongly they are attached to their own opinion [19]. Empirical research suggests that individuals are not homogeneous in influence perception [9], and the way they distribute trust over their communication networks may depend on many factors, both demographic (age, gender, education level, etc.) and structural (the number of friends, the number of common friends, betweenness centrality, etc.).…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain

Gezha

Kozitsin

2023

Computers

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The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention.

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Change My Mind: Data Driven Estimate of Open-Mindedness from Political Discussions

Cited by 5 publications

References 29 publications

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Mass Media Impact on Opinion Evolution in Biased Digital Environments: a Bounded Confidence Model

The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain

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