Twitter is a common site of offensive language. Prior literature has shown that the emotional content of tweets can heavily impact their diffusion when discussing political topics. We extend prior work to look at offensive tweets that link to academic articles. Using a mixed methods approach, we identify three findings: firstly, offensive language is common in tweets that refer to academic articles, and vary widely by subject matter. Secondly, discourse analysis reveals that offensive tweets commonly use academic articles to promote or attack political ideologies. Lastly, we show that offensive tweets reach a smaller audience than their non-offensive counterparts. Our analysis of these offensive tweets reveal how academic articles are being shared on Twitter not for the sake of disseminating new knowledge, but rather to as argumentative tools in controversial and combative discourses. We end by suggesting platform interventions to dissuade abusive discussions of science on social media.

Increasingly, crowdfunding is transforming financing for many people worldwide. Yet we know relatively little about how, why, and when funding outcomes are impacted by signaling between funders. We conduct two studies of \(N=500\) and \(N=750\) participants involved in crowdfunding to investigate the effect of “crowd signals,” i.e., certain characteristics deduced from the amounts and timing of contributions, on the decision to fund. In our first study, we find that, under a variety of conditions, contributions of heterogeneous amounts arriving at varying time intervals are significantly more likely to be selected than homogeneous contribution amounts and times. The impact of signaling is strongest among participants who are susceptible to social influence. The effect is remarkably general across different project types, fundraising goals, participant interest in the projects, and participants’ altruistic attitudes. Our second study using less strict controls indicates that the role of crowd signals in decision-making is typically unrecognized by participants. Our results underscore the fundamental nature of social signaling in crowdfunding. They highlight the importance of designing around these crowd signals and inform user strategies both on the project creator and funder side.

Town hall-type debates are increasingly moving online, irrevocably transforming public discourse. Yet, we know relatively little about crucial social dynamics that determine which arguments are more likely to be successful. This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates. We propose a novel framework for measuring the relationship between network position and persuasiveness, using a combination of social network analysis and machine learning. Complementing existing studies investigating the effect of linguistic aspects on persuasiveness, we show that the user's position in a discussion network is associated with their persuasiveness online. Moreover, the recognition of successful persuasion is linked to an increase in dominant network position. Our findings offer important insights into the complex social dynamics of online discourse and provide practical insights for organizations and individuals seeking to understand the interplay between influential positions in a discussion network and persuasive strategies in digital spaces.

The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article's abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time---posing a critical concern for researchers, policymakers, and citizens alike---but suggest that multi-platform discussions may improve information retention overall.

Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover universal crowd dynamics that ultimately decide which projects will succeed. In all analyses and across three markets, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.

We propose a network-aware multi-agent simulation approach to understanding the interlacing connections between herder-farmer communities in open property regimes. Specifically, we model herder-farmer conflicts in agent-based terms whereby individual decision-making, pastoral mobility, and symbiotic herder-farmer relations result in the emergence of a complex adaptive system in which communal resources are managed in ways that either lead to peaceful coexistence or conflict. From a theoretical perspective, we hope to further understanding of how individual decision-making and coordination produces complex adaptive systems as well as how emergent structures shape individual action. In practice, we anticipate that this study will help shed light on how herder-farmer communities can cooperate and coordinate their activity and mobility patterns to manage common pool resources in sustainable ways that mitigate violent conflict. Broadly, our work aims to contribute new insights towards multi-agent modelling of traditional small-scale societies.

Civil wars are as frequent and debilitating now as ever. More often than not, their resolution consists of the negotiation of a peace accord that involves a number of provisions. Although previous work in political science indicates an underlying interdependence between provision implementation sequences, it is unclear how the structure and dynamics of this interdependence relate to the successful implementation of peace accords. To fill this gap, we systematically study peace process implementation activity from 34 peace accords containing 51 provisions negotiated between 1989 and 2015. We begin by constructing a bipartite network between peace accords and their provisions' implementation and explore statistical properties of the structural underpinnings of peace processes. Then, we examine motifs (i.e., significantly frequent patterns) in provision implementation activity and uncover higher order correlations between provisions. Finally, we identify provision implementation sequences (i.e., meta-groups) that are most strongly associated with successful peace processes. Our empirical findings provide new insights for the implementation of peace accords by revealing temporal sequences of peace process implementation that help build confidence, enhance security, and ultimately prevent negative cascading effects in different stages of the peacebuilding process.

In recent years crowdfunding has diversified and grown beyond most experts' projections. Originally aiming to serve venture ideas and entrepreneurs outside the focus of traditional capital markets, the crowdfunding marketplace has developed a complicated relationship with novel ideas. Yet, there is little to no research on the relationship between project novelty and success in crowdfunding. This paper measures the novelty of crowdfunding campaigns using the content and language of their pitches, capturing their tendency to combine different venture sectors and topics in distinctive ways. Using a unique data set that covers four years of activity on a leading equity crowdfunding platform, we investigate the link between novelty and success, as well as how novelty appeals to different kinds of investors. We find that novelty derived from campaign pitches is negatively related with fundraising success even when controlling for quality and style of writing. We also find that novel campaigns are more likely to attract less-frequent, large-sum investors. Our findings contribute to the long-standing debate related to the trade-offs between innovativeness and conventionality in maximizing chances of startup survival. Our results also have important implications for entrepreneurs writing fundraising pitches and for platform providers who wish to facilitate successful innovation.

Online peer-to-peer investment platforms are increasingly popular venues for entrepreneurs and investors to engage in financial transactions without the involvement of banks and loan managers. Despite their purported transparency and lack of bias, it is unclear whether social inequalities present in traditional capital markets transfer to these platforms as well, impeding their hoped revolutionary potential. In this paper we analyze nearly four years' worth of data from one of the leading UK-based equity crowdfunding platforms. Specifically, we investigate gender-related differences in patterns of entrepreneurship, investment, and success. In agreement with offline trends, men have more activity on the platform. Yet, women entrepreneurs benefit of higher success rates in fund-raising, a finding that mimics trends seen on some rewards-based crowdfunding platforms. Surprisingly, we also find that female investors tend to choose campaigns that have lower success rates. Our findings contribute to a better understanding of gender-related discrepancies in success on the online capital market and point to differences in activity that are key factors in the apparent patterns of gender inequality.

Crowds offer a new form of efficacious collective decision making, yet knowledge about the mechanisms by which they achieve superior outcomes remains nascent. It has been suggested that crowds work best with market-like relationships when individuals make independent decisions and possess dissimilar information. By contrast, sociological discussions of markets argue that risky decisions are mitigated by network relations that embed economic transactions in social ties that promote trustworthiness and reciprocity. To investigate the role of networks within crowds and their performance effects, we examined the complete record of financial lending decisions on Prosper.com, 1/2006-3/2012, the first U.S. crowdfunding platform and a chief gateway to capital for entrepreneurs and general borrowers that continues to disrupt conventional financial lending structures infusing more than $5.1 billion into the market in 2013. Our study reveals how reciprocity, recurring borrower-lender dyads, and persistent co-lending underpin the dynamics of network lending. Further, we show how network ties influence the evolution of the lending behavior. We find that in the early stage of fundraising, network relations provide larger proportions of loans, typically lending four times more per bid than strangers. They also respond to loan requests on average 59.5% sooner than strangers. The size of the first loan and the time to lending also tend to prompt lending by strangers, suggesting that network relations might move the market, a finding that persists even as fewer lenders dominate more of the market for loans on Prosper. Finally, network relations are associated with greater engagement: when the first loan is underwritten by a friend, 50% of the remaining loans come from friends as well.

SUMMARY: Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements of two different types such as the influence of different experimental conditions on cellular variables or the direct interaction between receptors and their activators/inhibitors. One of the major challenges in the analysis of such noisy datasets is the statistical evaluation of the relationship between any two elements of the same type. Here, we present SICOP (significant co-interaction patterns), an implementation of a method that provides such an evaluation based on the number of their common interaction partners, their so-called co-interaction. This general network analytic method, proved successful in diverse fields, provides a framework for assessing the significance of this relationship by comparison with the expected co-interaction in a suitable null model of the same bipartite graph. SICOP takes into consideration up to two distinct types of interactions such as up- or downregulation. The tool is written in Java and accepts several common input formats and supports different output formats, facilitating further analysis and visualization. Its key features include a user-friendly interface, easy installation and platform independence. AVAILABILITY: The software is open source and available at cna.cs.uni-kl.de/SICOP under the terms of the GNU General Public Licence (version 3 or later).

Several important social network data sets have an inherent bipartite structure: for example, agents are affiliated with societies, authors write articles, customers buy, rent, or rate products. One commonly used network analytic approach to their analysis involves projecting them, i.e., deducing relations between actors of the same type (e.g. societies, articles, or products). Some of the available large scale data sets not only represent one, but several distinct relations between the same actors thereby calling for a projection method that accounts for the multiple nature of the relations. In this article we present a statistical method that properly extends a projection algorithm developed for bipartite networks containing one single type of relation. We show the stability of the proposed method on synthetic data. Then, we apply it to a real-world network of users rating films, namely a subset of the Netflix prize data set. We show that there is a gain from differentiating between the relation types. Based on the assumption that co-ratings of films contain information about the films' similarity, we analyze the co-liking and co-disliking structures obtained by the new one-mode projection. We find that the projections of concordant ratings show a high clustering coefficient while discordant co-ratings have a very small one. This result indicates that the assumption is valid and that thus the new one-mode projection can be used as basis for recommendations.