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Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content.

The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption.

The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations.

The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.

In March 2019, Facebook updated its security procedures requesting ID verification for people who wish to advertise or promote political posts of adverts. The announcement received little media coverage even though it is an interesting development in the battle against fake news. This paper aims to review the current literature on different approaches in the battle against the spread of fake news, including the use of computer algorithms, artificial intelligence (AI) and introduction of ID checks.

Critical to the evaluation is consideration into ID checks as a means to combat the spread of fake news. To understand the process and how it works, the team undertook a social experiment combined with reflective analysis to better understand the impact of ID check policies when combined with other standards policies of a typical platform.

The analysis identifies grave concerns. In a wider context, standardising such policy will leave political activists in countries vulnerable to reprisal from authoritarian regimes. Other victims of the impacts include people who use fake names to protect the identity of adopted children or to protect anonymity from abusive partners.

The analysis also points to the fact that troll armies could bypass these checks rendering the use of ID checks less effective in the battle to combat fake news.

The socializing of hate and its saturation on platforms as a resonant and emotional connection online reveal the networked nature of convergent platforms which pump hate as a mechanism of connection and fracture in society in the post-digital age. The violence of hate and negative sentiments online morph to appropriate a multitude of manifestations from cyberbullying and revenge porn to trolling and memes as subversive, denigrative humour. Social media, designed through an architecture for sharing and transaction, distributes hate as a popular sentiment, building connections with disparate communities through the articulation of hate for fellow humans and humanity at large. Trauma induced through hatred and bullying as an active aspect of social media platforms and interactivity distribute sentiments through its excess and disproportionality. This chapter interrogates the sentiment of hate and its workings on social media as a technology of trauma in distributing hate as a form of communion.

This conceptual paper explores the implications for the sociology of the professions of robo-advice (RA) provided by robo-advisors (RAs) as an early example of successfully programmed algorithmic knowledge managed by artificial intelligence (AI).

The authors examine the drivers of RAs, their success, characteristics, and establish RA as an early precursor of commercialized, programmed professional advice with implications for developments in the sociology of the professions.

Within the lens of the sociology of the professions, the success of RAs suggests that the diffusion of this innovation depends on three factors: the programmed flows of automated professional knowledge are minimally disruptive, they are less costly, and attract attention because of the “on-trend” nature of algorithmic authority guided by AI. The on-trend nature of algorithmic governance and its increasing public acceptance points toward an algorithmic paradox. The contradictions arise in the gap between RA marketed to the public and as a set of professional practices.

The incursion of RA-like disembodied advice into other professions is predicted given the emergence of tech-savvy clients, the tie between RA and updatable flows of big data, and an increasing shift to the “maker” or “do-it-yourself” movements.

Using the success of RAs in the financial industry, the authors predict that an AI-managed platform, despite the algorithmic paradox, is an avenue for growth with implications for researchers in the sociology of the professions.

In the face of increasing political polarization worldwide, this study explores whether people create biased perceptions of political knowledge and how this affects their selection and evaluation of political content on YouTube.

For this study, an online experiment was conducted with 441 panels of South Korean respondents. In the first phase, participants answered 10 questions designed to capture their level of objective political knowledge, and for each question, they indicated whether they had responded to that question correctly as a means of measuring their subjective political knowledge. In the second phase, two types of YouTube thumbnails were presented to represent progressive and conservative claims on two controversial political issues, and participants rated and selected the content they would like to see.

Participants with low political knowledge perceived their knowledge as more than it really was. In contrast, participants with high political knowledge perceived their political knowledge as less than it really was. This biased perception of political knowledge influences respondents' choice and evaluation of political YouTube channel videos.

At a time when political polarization is increasing around the world, this study sought to explore how perceptions of political knowledge differ from actual political knowledge by applying the Dunning-Kruger effect. The authors also used political YouTube channels, whose role in forming public opinion and political influence is rapidly growing, to study the behavior and attitudes of a group of Korean respondents in the media according to their actual and perceived level of political literacy.

The purpose of this paper is twofold. First, the authors investigate the main drivers of house prices in the Hong Kong housing market. Second, further research is undertaken to confirm the existence of house price overvaluation, which has driven the market into a bubble episode.

First, the authors propose a theoretical framework to identify the fundamentals of the market. In the second step, they decompose house prices into fundamentals, frictions and bubble episodes for a better understanding of the evolution of house prices during the period 1996(Q3)-2013(Q3).

The results of this paper suggest an eventual possible correction of up to 46 per cent of house prices with respect to their 2013(Q3) level.

The originality of this paper is to use the procedure developed by Glindro and Delloro (2010) to analyse the Hong Kong housing market. The contribution of this paper also modifies the original Glindro and Delloro’s (2010) approach by including the Christiano and Fitzgerald (2003) filter to decompose house prices.

This chapter aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as locally explosive episodes (denoted here as bubbles) in a strictly stationary setting. The authors consider multiple detrending methods and investigate, using Monte Carlo simulations, to what extent they preserve the bubble patterns observed in the raw data. MAR models relies on the dynamics observed in the series alone and does not require economical background to construct a structural model, which can sometimes be intricate to specify or which may lack parsimony. The authors investigate oil prices and estimate probabilities of crashes before and during the first 2020 wave of the COVID-19 pandemic. The authors consider three different mechanical detrending methods and compare them to a detrending performed using the level of strategic petroleum reserves.