Science vs Conspiracy: Collective Narratives in the Age of Misinformation

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.     

Trend of Narratives in the Age of Misinformation

Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking—e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile clusters—i.e., echo-chambers. Along this path we study, by means of a thorough quantitative analysis, how different topics are consumed inside the conspiracy echo-chamber in the Italian Facebook. Through a semi-automatic topic extraction strategy, we show that the most consumed contents semantically refer to four specific categories: environment, diet, health, and geopolitics. We find similar consumption patterns by comparing users activity (likes and comments) on posts belonging to these different semantic categories. Finally, we model users mobility across the distinct topics finding that the more a user is active, the more he is likely to span on all categories. Once inside a conspiracy narrative users tend to embrace the overall corpus.     

Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network

To study the sentiment diffusion of online public opinions about hot events, we collected people’s posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts’ sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people’s sentiments regarding online hot events according to the sentiment diffusion mechanism.     

Evidence of Online Performance Deterioration in User Sessions on Reddit

This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.     

Studying User Income through Language,Behaviour and Affect in Social Media

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.     

Examining Perceptions about Mandatory Influenza Vaccination of Healthcare Workers through Online Comments on News Stories

Background

The aim of this study was to understand online public perceptions of the debate surrounding the choice of annual influenza vaccinations or wearing masks as a condition of employment for healthcare workers, such as the one enacted in British Columbia in August 2012.

Methods

Four national and 82 local (British Columbia) Canadian online news sites were searched for articles posted between August 2012 and May 2013 containing the words “healthcare workers” and “mandatory influenza vaccinations/immunizations” or “mandatory flu shots and healthcare workers.” We included articles from sources that predominantly concerned our topic of interest and that generated reader comments. Two researchers coded the unedited comments using thematic analysis, categorizing codes to allow themes to emerge. In addition to themes, the comments were categorized by: 1) sentiment towards influenza vaccines; 2) support for mandatory vaccination policies; 3) citing of reference materials or statistics; 4) self-identified health-care worker status; and 5) sharing of a personal story.

Results

1163 comments made by 648 commenters responding to 36 articles were analyzed. Popular themes included concerns about freedom of choice, vaccine effectiveness, patient safety, and distrust in government, public health, and the pharmaceutical industry. Almost half (48%) of commenters expressed a negative sentiment toward the influenza vaccine, 28% were positive, 20% were neutral, and 4% expressed mixed sentiment. Of those who commented on the policy, 75% did not support the condition to work policy, while 25% were in favour. Of the commenters, 11% self-identified as healthcare workers, 13% shared personal stories, and 18% cited a reference or statistic.

Interpretation

The perception of the influenza vaccine in the comment sections of online news sites is fairly poor. Public health agencies should consider including online forums, comment sections, and social media sites as part of their communication channels to correct misinformation regarding the benefits of HCW influenza immunization and the effectiveness of the vaccine.     

Red,Purple and Pink: The Colors of Diffusion on Pinterest

Many lab studies have shown that colors can evoke powerful emotions and impact human behavior. Might these phenomena drive how we act online? A key research challenge for image-sharing communities is uncovering the mechanisms by which content spreads through the community. In this paper, we investigate whether there is link between color and diffusion. Drawing on a corpus of one million images crawled from Pinterest, we find that color significantly impacts the diffusion of images and adoption of content on image sharing communities such as Pinterest, even after partially controlling for network structure and activity. Specifically, Red, Purple and pink seem to promote diffusion, while Green, Blue, Black and Yellow suppress it. To our knowledge, our study is the first to investigate how colors relate to online user behavior. In addition to contributing to the research conversation surrounding diffusion, these findings suggest future work using sophisticated computer vision techniques. We conclude with a discussion on the theoretical, practical and design implications suggested by this work—e.g. design of engaging image filters.     

Reality Check for the Chinese Microblog Space: A Random Sampling Approach

Chinese microblogs have drawn global attention to this online application’s potential impact on the country’s social and political environment. However, representative and reliable statistics on Chinese microbloggers are limited. Using a random sampling approach, this study collected Chinese microblog data from the service provider, analyzing the profile and the pattern of usage for 29,998 microblog accounts. From our analysis, 57.4% (95% CI 56.9%,58.0%) of the accounts’ timelines were empty. Among the 12,774 non-zero statuses samples, 86.9% (95% CI 86.2%,87.4%) did not make original post in a 7-day study period. By contrast, 0.51% (95% CI 0.4%,0.65%) wrote twenty or more original posts and 0.45% (95% CI 0.35%,0.60%) reposted more than 40 unique messages within the 7-day period. A small group of microbloggers created a majority of contents and drew other users’ attention. About 4.8% (95% CI 4.4%,5.2%) of the 12,774 users contributed more than 80% (95% CI,78.6%,80.3%) of the original posts and about 4.8% (95% CI 4.5%,5.2%) managed to create posts that were reposted or received comments at least once. Moreover, a regression analysis revealed that volume of followers is a key determinant of creating original microblog posts, reposting messages, being reposted, and receiving comments. Volume of friends is found to be linked only with the number of reposts. Gender differences and regional disparities in using microblogs in China are also observed.     

Social network- and community-level influences on contraceptive use: evidence from rural Poland

The diffusion of ‘modern’ contraceptives—as a proxy for the spread of low-fertility norms—has long interested researchers wishing to understand global fertility decline. A fundamental question is how local cultural norms and other people''s behaviour influence the probability of contraceptive use, independent of women''s socioeconomic and life-history characteristics. However, few studies have combined data at individual, social network and community levels to simultaneously capture multiple levels of influence. Fewer still have tested if the same predictors matter for different contraceptive types. Here, we use new data from 22 high-fertility communities in Poland to compare predictors of the use of (i) any contraceptives—a proxy for the decision to control fertility—with those of (ii) ‘artificial’ contraceptives—a subset of more culturally taboo methods. We find that the contraceptive behaviour of friends and family is more influential than are women''s own characteristics and that community level characteristics additionally influence contraceptive use. Highly educated neighbours accelerate women''s contraceptive use overall, but not their artificial method use. Highly religious neighbours slow women''s artificial method use, but not their contraceptive use overall. Our results highlight different dimensions of sociocultural influence on contraceptive diffusion and suggest that these may be more influential than are individual characteristics. A comparative multilevel framework is needed to understand these dynamics.     

Social Identity Threat Motivates Science-Discrediting Online Comments

Experiencing social identity threat from scientific findings can lead people to cognitively devalue the respective findings. Three studies examined whether potentially threatening scientific findings motivate group members to take action against the respective findings by publicly discrediting them on the Web. Results show that strongly (vs. weakly) identified group members (i.e., people who identified as “gamers”) were particularly likely to discredit social identity threatening findings publicly (i.e., studies that found an effect of playing violent video games on aggression). A content analytical evaluation of online comments revealed that social identification specifically predicted critiques of the methodology employed in potentially threatening, but not in non-threatening research (Study 2). Furthermore, when participants were collectively (vs. self-) affirmed, identification did no longer predict discrediting posting behavior (Study 3). These findings contribute to the understanding of the formation of online collective action and add to the burgeoning literature on the question why certain scientific findings sometimes face a broad public opposition.     

Emoticon-Based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo

Recent decades have witnessed online social media being a big-data window for testifying conventional social theories quantitatively and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden “ambivalent users” are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights and at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift in ambivalent tweets is more evident than usual and exhibits a clear “negative to positive” pattern. The above observations, though being promiscuous seemingly, actually point to the self-regulation of negative mood in Weibo, which could find its basis from the traditional emotion management theories in sociology but makes an important extension to the online environment in this study. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.     

Consequences of plant invasions on compartmentalization and species’ roles in plant–pollinator networks

Compartmentalization—the organization of ecological interaction networks into subsets of species that do not interact with other subsets (true compartments) or interact more frequently among themselves than with other species (modules)—has been identified as a key property for the functioning, stability and evolution of ecological communities. Invasions by entomophilous invasive plants may profoundly alter the way interaction networks are compartmentalized. We analysed a comprehensive dataset of 40 paired plant–pollinator networks (invaded versus uninvaded) to test this hypothesis. We show that invasive plants have higher generalization levels with respect to their pollinators than natives. The consequences for network topology are that—rather than displacing native species from the network—plant invaders attracting pollinators into invaded modules tend to play new important topological roles (i.e. network hubs, module hubs and connectors) and cause role shifts in native species, creating larger modules that are more connected among each other. While the number of true compartments was lower in invaded compared with uninvaded networks, the effect of invasion on modularity was contingent on the study system. Interestingly, the generalization level of the invasive plants partially explains this pattern, with more generalized invaders contributing to a lower modularity. Our findings indicate that the altered interaction structure of invaded networks makes them more robust against simulated random secondary species extinctions, but more vulnerable when the typically highly connected invasive plants go extinct first. The consequences and pathways by which biological invasions alter the interaction structure of plant–pollinator communities highlighted in this study may have important dynamical and functional implications, for example, by influencing multi-species reciprocal selection regimes and coevolutionary processes.     

Phenotypic-dependent variability and the emergence of tolerance in bacterial populations

Ecological and evolutionary dynamics have been historically regarded as unfolding at broadly separated timescales. However, these two types of processes are nowadays well-documented to intersperse much more tightly than traditionally assumed, especially in communities of microorganisms. Advancing the development of mathematical and computational approaches to shed novel light onto eco-evolutionary problems is a challenge of utmost relevance. With this motivation in mind, here we scrutinize recent experimental results showing evidence of rapid evolution of tolerance by lag in bacterial populations that are periodically exposed to antibiotic stress in laboratory conditions. In particular, the distribution of single-cell lag times—i.e., the times that individual bacteria from the community remain in a dormant state to cope with stress—evolves its average value to approximately fit the antibiotic-exposure time. Moreover, the distribution develops right-skewed heavy tails, revealing the presence of individuals with anomalously large lag times. Here, we develop a parsimonious individual-based model mimicking the actual demographic processes of the experimental setup. Individuals are characterized by a single phenotypic trait: their intrinsic lag time, which is transmitted with variation to the progeny. The model—in a version in which the amplitude of phenotypic variations grows with the parent’s lag time—is able to reproduce quite well the key empirical observations. Furthermore, we develop a general mathematical framework allowing us to describe with good accuracy the properties of the stochastic model by means of a macroscopic equation, which generalizes the Crow-Kimura equation in population genetics. Even if the model does not account for all the biological mechanisms (e.g., genetic changes) in a detailed way—i.e., it is a phenomenological one—it sheds light onto the eco-evolutionary dynamics of the problem and can be helpful to design strategies to hinder the emergence of tolerance in bacterial communities. From a broader perspective, this work represents a benchmark for the mathematical framework designed to tackle much more general eco-evolutionary problems, thus paving the road to further research avenues.     

Dr. Manners

Good manners make a difference—in science and elsewhere. This includes our social media etiquette as researchers. Subject Categories: S&S: History & Philosophy of Science, Methods & Resources, S&S: Ethics

Elbows off the table, please. Don’t chew with your mouth open. Don’t blow your nose at the table. Don’t put your feet up on the chair or table. And please, do not yuck my yum. These are basic table manners that have come up at some of our lab meals, and I have often wondered if it was my job to teach my trainees social graces. A good fellow scientist and friend of mine once told me it was absolutely our place as mentors to teach our trainees not only how to do science well, but also how to be well‐mannered humans. While these Emily Post‐approved table manners might seem old‐fashioned (I’m guessing some readers will have to look up Emily Post), I strongly believe they still hold a place in modern society; being in good company never goes out of style.Speaking of modern society: upon encouragement by several of my scientist friends, I joined Twitter in 2016. My motivation was mainly to hear about pre‐prints and publications, conference announcements and relevant news, science or otherwise. I also follow people who just make me laugh (I highly recommend @ConanOBrien or @dog_rates). I (re)tweet job openings, conference announcements, and interesting new data. Occasionally, I post photos from conferences, or random science‐related art. I also appreciate the sense of community that social media brings to the table. However, social media is a venue where I have also seen manners go to die. Rapidly.It is really shocking to read what some people feel perfectly comfortable tweeting. While most of us can agree that foul language and highly offensive opinions are generally considered distasteful, there are other, subtler but nonetheless equally—if not more—cringe‐worthy offenses online when I am fairly certain these people would never utter such words in real life. In the era of pandemic, the existence of people tweeting about not being able to eat at their favorite restaurant or travel to some destination holiday because of lockdown shows an egregious lack of self‐awareness. Sure it sucks to cancel a wedding due to COVID‐19, but do you need to moan to your followers—most of whom are likely total strangers—about it while other people have lost their jobs? If I had a nickel for every first‐world complaint I have seen on Twitter, I’d have retired a long time ago; although to be honest, I would do science for free. However, these examples pale in comparison with another type of tweeter: Reader, I submit to you, “the Humblebragger.”From the MacMillan Buzzword dictionary (via Google): a humblebrag is “a statement in which you pretend to be modest but which you are really using as a way of telling people about your success or achievements.” I would further translate this definition to indicate that humblebraggers are starved for attention. After joining Twitter, I quickly found many people using social media to announce how “humble and honored” they are for receiving grant or prize X, Y, or Z. In general, these are junior faculty who have perhaps not acquired the self‐awareness more senior scientists have. Perhaps the most off‐putting posts I have seen are from people who post photos of their NIH application priority scores right after study section, or their Notice of Awards (NOA). When did we ever, before social media, send little notes to each other—let alone to complete strangers—announcing our priority scores or NOAs? (Spoiler: NEVER)Some of you reading this opinion piece might have humblebragged at one or time or another, and might not understand why it is distasteful. Please let me explain. For every person who gets a fundable score, there are dozens more people who do not, and they are sad (I speak from many years of experience). While said fundable‐score person might be by someone we like—and I absolutely, positively wish them well—there are many more people who will feel lousy because they did not get funding from the same review round. When has anyone ever felt good about other people getting something that they, too, desire? I think as children, none of us liked the kid on the playground who ran around with the best new Toy of the Season. As adults, do we feel differently? Along these lines, I have never been a fan of “best poster/talk/abstract” prizes. Trainees should not be striving for these fleeting recognitions and should focus on doing the best science for Science’s sake; I really believe this competition process sets people up for life in a negative way—there, I’ve said it.Can your friends and colleagues tweet about your honors? Sure, why not, and by all means please let your well‐wishers honor you, and do thank them and graciously congratulate your trainees or colleagues for helping you to get there. But to post things yourself? Please. Don’t be surprised if you have been muted by many of your followers.It is notable that many of our most decorated scientists are not on Twitter, or at least never tweet about their accomplishments. I do not recall ever seeing a single Nobel laureate announce how humbled and honored they are about their prize. Of course, I might be wrong, but I am willing to bet the numbers are much lower than what I have observed for junior faculty. True humility will never be demonstrated by announcing your achievements to your social media followers, and I believe humblebragging reveals insecurity more than anything. I hope that many more of us can follow the lead of our top scientists both in creativity, rigor, and social media politeness.     

Anger Is More Influential than Joy: Sentiment Correlation in Weibo

Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than five years. Connected by online social ties, different users might share similar affective states. We find that the correlation of anger among users is significantly higher than that of joy. While the correlation of sadness is surprisingly low. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends possess more significant sentiment correlation with their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.     

Correlated Evolution of Nucleotide Positions within Splice Sites in Mammals

Splice sites (SSs)—short nucleotide sequences flanking introns—are under selection for spliceosome binding, and adhere to consensus sequences. However, non-consensus nucleotides, many of which probably reduce SS performance, are frequent. Little is known about the mechanisms maintaining such apparently suboptimal SSs. Here, we study the correlations between strengths of nucleotides occupying different positions of the same SS. Such correlations may arise due to epistatic interactions between positions (i.e., a situation when the fitness effect of a nucleotide in one position depends on the nucleotide in another position), their evolutionary history, or to other reasons. Within both the intronic and the exonic parts of donor SSs, nucleotides that increase (decrease) SS strength tend to co-occur with other nucleotides increasing (respectively, decreasing) it, consistent with positive epistasis. Between the intronic and exonic parts of donor SSs, the correlations of nucleotide strengths tend to be negative, consistent with negative epistasis. In the course of evolution, substitutions at a donor SS tend to decrease the strength of its exonic part, and either increase or do not change the strength of its intronic part. In acceptor SSs, the situation is more complicated; the correlations between adjacent positions appear to be driven mainly by avoidance of the AG dinucleotide which may cause aberrant splicing. In summary, both the content and the evolution of SSs is shaped by a complex network of interdependences between adjacent nucleotides that respond to a range of sometimes conflicting selective constraints.     

Morphological complexity affects the diversity of marine microbiomes

Large eukaryotes support diverse communities of microbes on their surface—epibiota—that profoundly influence their biology. Alternate factors known to structure complex patterns of microbial diversity—host evolutionary history and ecology, environmental conditions and stochasticity—do not act independently and it is challenging to disentangle their relative effects. Here, we surveyed the epibiota from 38 sympatric seaweed species that span diverse clades and have convergent morphology, which strongly influences seaweed ecology. Host identity explains most of the variation in epibiont communities and deeper host phylogenetic relationships (e.g., genus level) explain a small but significant portion of epibiont community variation. Strikingly, epibiota community composition is significantly influenced by host morphology and epibiota richness increases with morphological complexity of the seaweed host. This effect is robust after controlling for phylogenetic non-independence and is strongest for crustose seaweeds. We experimentally validated the effect of host morphology by quantifying bacterial community assembly on latex sheets cut to resemble three seaweed morphologies. The patterns match those observed in our field survey. Thus, biodiversity increases with habitat complexity in host-associated microbial communities, mirroring patterns observed in animal communities. We suggest that host morphology and structural complexity are underexplored mechanisms structuring microbial communities.Subject terms: Microbial ecology, Biodiversity     

Inferring Tie Strength from Online Directed Behavior

Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (specifically, Facebook interactions) to successfully identify real-world strong ties. Ground truth was established by asking users themselves to name their closest friends in real life. We found the frequency of online interaction was diagnostic of strong ties, and interaction frequency was much more useful diagnostically than were attributes of the user or the user’s friends. More private communications (messages) were not necessarily more informative than public communications (comments, wall posts, and other interactions).     

A system for de-identifying medical message board text

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients' experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors' personal lives. The main contribution of this paper is a system to de-identify the authors of message board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available named entity recognition and de-identification systems, which have been tuned for more structured text like operative reports, pathology reports, discharge summaries, or newswire.     

Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.