Does engagement with opposing views help break down ideological `echo chambers; or does it backfire and reinforce them? This question remains critical as academics, policymakers and activists grapple with the question of how to regulate political dis
cussion on social media. In this study, we contribute to the debate by examining the impact of opposing views within a major climate change skeptic online community on Reddit. A large sample of posts (N = 3000) was manually coded as either dissonant or consonant which allowed the automated classification of the full dataset of more than 50,000 posts, with codes inferred from linked websites. We find that ideologically dissonant submissions act as a stimulant to activity in the community: they received more attention (comments) than consonant submissions, even though they received lower scores through up-voting and down-voting. Users who engaged with dissonant submissions were also more likely to return to the forum. Consistent with identity theory, confrontation with opposing views triggered activity in the forum, particularly among users that are highly engaged with the community. In light of the findings, theory of social identity and echo chambers is discussed and enhanced.
The theory of echo chambers, which suggests that online political discussions take place in conditions of ideological homogeneity, has recently gained popularity as an explanation for patterns of political polarization and radicalization observed in
many democratic countries. However, while micro-level experimental work has shown evidence that individuals may gravitate towards information that supports their beliefs, recent macro-level studies have cast doubt on whether this tendency generates echo chambers in practice, instead suggesting that cross-cutting exposures are a common feature of digital life. In this article, we offer an explanation for these diverging results. Building on cognitive dissonance theory, and making use of observational trace data taken from an online white nationalist website, we explore how individuals in an ideological echo chamber engage with opposing viewpoints. We show that this type of exposure, far from being detrimental to radical online discussions, is actually a core feature of such spaces that encourages people to stay engaged. The most common echoes in this echo chamber are in fact the sound of opposing viewpoints being undermined and marginalized. Hence echo chambers exist not only in spite of but thanks to the unifying presence of oppositional viewpoints. We conclude with reflections on policy implications of our study for those seeking to promote a more moderate political internet.
Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the e
xtent to which traffic disruption can be estimated from static features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with static features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSMs granular point of interest data allows for better predictions than the aggregate categories typically used in studies of transportation and land use.
Accurate modelling of local population movement patterns is a core contemporary concern for urban policymakers, affecting both the short term deployment of public transport resources and the longer term planning of transport infrastructure. Yet, whil
e macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform badly in smaller geographic confines. In this paper we take a first step to remedying this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down at small scales. In particular, we use an anonymised aggregate dataset from a major mobility app and combine this with freely available data from OpenStreetMap concerning land-use composition of different areas around the county of Oxfordshire in the United Kingdom. We show where different models fail, and make the case for a new modelling strategy which moves beyond rough heuristics such as distance and population size towards a detailed, granular understanding of the opportunities presented in different areas of the city.
The emergence of large stores of transactional data generated by increasing use of digital devices presents a huge opportunity for policymakers to improve their knowledge of the local environment and thus make more informed and better decisions. A re
search frontier is hence emerging which involves exploring the type of measures that can be drawn from data stores such as mobile phone logs, Internet searches and contributions to social media platforms, and the extent to which these measures are accurate reflections of the wider population. This paper contributes to this research frontier, by exploring the extent to which local commuting patterns can be estimated from data drawn from Twitter. It makes three contributions in particular. First, it shows that simple heuristics drawn from geolocated Twitter data offer a good proxy for local commuting patterns; one which outperforms the major existing method for estimating these patterns (the radiation model). Second, it investigates sources of error in the proxy measure, showing that the model performs better on short trips with higher volumes of commuters; it also looks at demographic biases but finds that, surprisingly, measurements are not significantly affected by the fact that the demographic makeup of Twitter users differs significantly from the population as a whole. Finally, it looks at potential ways of going beyond simple heuristics by incorporating temporal information into models.
