standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| contribution | contribution | what the paper claims to add |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
Science vs Conspiracy: Collective Narratives in the Age of Misinformation
Abstract
object_of_studyclaim_typephenomenonconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concept_edgesThe 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.
Introduction
concepts↗concepts↗The World Wide Web has changed the dynamics of information transmission as well as the agenda-setting process [ 1 ]. Relevance of facts, in particular when related to social relevant issues, mingle with half-truths and untruths to create informational blends [ 2 , 3 ]. In such a scenario, as pointed out by [ 4 ], individuals can be uninformed or misinformed and the role of corrections in the diffusion and formation of biased beliefs are not effective. In particular, in [ 5 ] online debunking campaigns have been shown to create a reinforcement effect in usual consumers of conspiracy stories. mechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗concept_edgesconcept_edgesIn this work, we address users consumption patterns of information using very distinct type of contents—i.e., main stream scientific news and conspiracy news. The former diffuse scientific knowledge and the sources are easy to access. The latter aim at diffusing what is neglected by manipulated main stream media. Specifically, conspiracy theses tend to reduce the complexity of reality by explaining significant social or political aspects as plots conceived by powerful individuals or organizations. Since these kinds of arguments can sometimes involve the rejection of science, alternative explanations are invoked to replace the scientific evidence. For instance, people who reject the link between HIV and AIDS generally believe that AIDS was created by the U.S. Government to control the African American population [ 6 ]. The spread of misinformation in such a context might be particularly difficult to detect and correct because of the social reinforcement—i.e. people are more likely to trust an information someway consistent with their system of beliefs [ 7 – 17 ]. mechanisms↗concepts↗concepts↗concept_edgesThe growth of knowledge fostered by an interconnected world together with the unprecedented acceleration of scientific progress has exposed the society to an increasing level of complexity to explain reality and its phenomena. Indeed, a shift of paradigm in the production and consumption of contents has occurred, utterly increasing the volumes as well as the heterogeneity of available to users. Everyone on the Web can produce, access and diffuse contents actively participating in the creation, diffusion and reinforcement of different narratives. Such a large heterogeneity of information fostered the aggregation of people around common interests, worldviews and narratives.
concepts↗concepts↗concept_edgesconcept_edgesNarratives grounded on conspiracy theories tend to reduce the complexity of reality and are able to contain the uncertainty they generate [ 18 – 20 ]. They are able to create a climate of disengagement from mainstream society and from officially recommended practices [ 21 ]—e.g. vaccinations, diet, etc. Despite the enthusiastic rhetoric about the collective intelligence [ 22 , 23 ] the role of socio-technical system in enforcing informed debates and their effects on the public opinion still remain unclear. However, the World Economic Forum listed massive digital misinformation as one of the main risks for modern society [ 24 ].
concepts↗concepts↗concept_edgesA multitude of mechanisms animates the flow and acceptance of false rumors, which in turn create false beliefs that are rarely corrected once adopted by an individual [ 8 , 10 , 25 , 26 ]. The process of acceptance of a claim (whether documented or not) may be altered by normative social influence or by the coherence with the system of beliefs if the individual [ 27 , 28 ]. A large body of literature addresses the study of social dynamics on socio-technical systems from social contagion up to social reinforcement [ 12 – 15 , 17 , 29 – 41 ].
Recently in [ 42 , 43 ] it has been shown that online unsubstantiated rumors—such as the link between vaccines and autism, the global warming induced by chem-trails or the secret alien government—and main stream information—such as scientific news and updates—reverberate in a comparable way. Pervasiveness of unreliable contents might lead to mix up unsubstantiated stories with their satirical counterparts—e.g. the presence of sildenafil-citratum (the active ingredient of Viagra™) [ 44 ] in chem-trails or the anti hypnotic effects of lemons (more than 45000 shares on Facebook) [ 45 , 46 ]. In fact, there are very distinct groups, namely trolls , building Facebook pages as a caricatural version of conspiracy news. Their activities range from controversial comments and posting satirical contents mimicking conspiracy news sources, to the fabrication of purely fictitious statements, heavily unrealistic and sarcastic. Not rarely, these memes became viral and were used as evidence in online debates from political activists [ 47 ].
limitationslimitationsconcepts↗sensesIn this work we target consumption patterns of users with respect to very distinct types of information. Focusing on the Italian context and helped by pages very active in debunking unsubstantiated rumors (see acknowledgment section), we build an atlas of scientific and conspiracy information sources on Facebook. Our dataset contains 271,296 post created by 73 Facebook pages. Pages are classified according to the kind of information disseminated and their self description in conspiracy news—alternative explanations of reality aiming at diffusing contents neglected by main stream information—and scientific news. For further details about the data collection and the dataset refer to the Methods section. Notice that it is not our intention claiming that conspiracy information are necessarily false. Our focus is on how communities formed around different information and narratives interact and consume their preferred information.
limitationsconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsunhoused_relationssensesIn the analysis, we account for user interaction with respect to pages public posts—i.e. likes, shares, and comments. Each of these actions has a particular meaning [ 48 – 50 ]. A like stands for a positive feedback to the post; a share expresses the will to increase the visibility of a given information; and comment is the way in which online collective debates take form around the topic promoted by posts. Comments may contain negative or positive feedbacks with respect to the post. research_questionconcepts↗concepts↗concepts↗claim_relationsOur analysis starts with an outline of information consumption patterns and the community structure of pages according to their common users. We label polarized users—users which their like activity (positive feedback) is almost (95%) exclusively on the pages of one category—and find similar interaction patterns on the two communities with respect to preferred contents. According to literature on opinion dynamics [ 37 ], in particular the one related to the Bounded confidence model (BCM) [ 51 ]—two individuals are able to influence each other only if the distance between their opinion is below a given distance—users consuming different and opposite information tend to aggregate into isolated clusters ( polarization ). Moreover, we measure their commenting activity on the opposite category finding that polarized users of conspiracy news are more focused on posts of their community and that they are more oriented on the diffusion of their contents—i.e. they are more prone to like and share posts from conspiracy pages. On the other hand, usual consumers of scientific news result to be less committed in the diffusion and more prone to comment on conspiracy pages. Finally, we test the response of polarized users to the exposure to 4709 satirical and demential version of conspiracy stories finding that, out of 3888 users labeled on likes and 3959 on comments, the most of them are usual consumers of conspiracy stories (80.86% of likes and 77.92% of comments). Our findings, coherently with [ 52 – 54 ] indicate that the relationship between beliefs in conspiracy theories and the need for cognitive closure—i.e. the attitude of conspiracists to avoid profound scrutiny of evidence to a given matter of fact—is the driving factors for the diffusion of false claims.
Results and discussion
In this work we address the driving forces behind the popularity of contents on online social media To do this, we start our analysis by characterizing users’ interaction patterns with respect to different kind of contents. Then, we label typical users according to the kind of information they are usually exposed to and validate their tolerance with respect to information that we know to be false as they are a parodistic imitation of conspiracy stories containing fictitious and heavily unrealistic statements.
Consumption patterns on science and conspiracy news
concepts↗Our analysis starts by looking at how Facebook users interact with contents from pages of conspiracy and mainstream scientific news. Fig. 1 shows the empirical complementary cumulative distribution function (CCDF) for likes (intended as positive feedbacks to the post), comments (a measure of the activity of online collective debates), and shares (intended as the the will to increase the visibility of a given information) for all posts produced by the different categories of pages. Distributions of likes, comments, and shares on both categories are heavy–tailed.
Users Activity. Empirical complementary cumulative distribution function (CCDF) of users’ activity (like, comment and share) for post grouped by page category. The distributions are indicating heavy–tailed consumption patterns for the various pages.
A post sets the attention on a given topic, then a discussion may evolve in the form of comments. To further investigate users consumption patterns, we zoom in at the level of comments. Such a measure is a good approximation of users attention with respect to the information reported on by the post. In Fig. 2 we show CCDF of the posts lifetime—i.e. the temporal distance between the first and the last comment for each post from the two categories of pages. Very distinct kinds of contents have have a comparable lifetime.
Post lifetime. Empirical complementary cumulative distribution function (CCDF), grouped by page category, of the temporal distance between the first and last comment to each post. The life time of posts in both categories is similar.
limitationsconcepts↗claim_relationsTo account for the distinctive features of the consumption patterns related to different contents, we focus on the correlation of combination of users’ interactions with posts. Likes and comments have a different meaning from a user viewpoint. Most of the time, a like stands for a positive feedback to the post; a share expresses the will to increase the visibility of a given information; and a comment is the way in which online collective debates take form and may contain negative or positive feedbacks with respect to the post. Notice that, cases in which they are motivated by ironic reasons are impossible to detect. In order to compute the correlation among different actions, we use the Pearson coefficient—i.e., the covariance of two variables (in this case couples of action) divided by the product of their standard deviations. In Table 1 we show the Pearson correlation for user couple of actions on posts (likes, comments and shares). As an example, a high correlation coefficient for Comments/Shares indicates that posts more commented are likely to be shared and vice versa.
Users Actions. Correlation (Pearson coefficient) between couple of actions to each post in scientific and conspiracy news. Posts from conspiracy pages are more likely to be liked and shared by users, indicating a major commitment in the diffusion.
Correlation values for posts of conspiracy news have higher values than those in science news. They receive more likes and shares, indicating a preference of conspiracy users to promote their liked contents. This finding is consistent with [ 52 – 54 ] which state that conspiracists need for cognitive closure, i.e. they are more likely to interact with conspiracy based theories and have a lower trust in other information sources. Qualitatively different information are consumed in a comparable way. However, zooming in at the combination of actions we find that users of conspiracy pages are more prone to share and like on a post. Such a latter result indicates a higher level of commitment of consumers of conspiracy news. They are more oriented to the diffusion of conspiracy related topics that are—according to their system of beliefs—neglected by main stream media and scientific news and consequently very difficult to verify. Such a pattern oriented to diffusion of conspiracy news opens to interesting about the pervasiveness of unsubstantiated rumors in online social media.
Information-based communities
concepts↗concepts↗The classification of pages in science and conspiracy related contents is grounded on their self-description and on the kind of promoted content (see the Methods section for further details and the list of pages). We want to understand if users engagement across very distinct contents shapes different communities around contents. We apply a network based approach aimed at measuring distinctive connectivity patterns of these information-based communities? i.e., users consuming information belonging to the same narrative. In particular, we transform data in order to have a bipartite network of pages and users—i.e., two pages are connected if a user liked a post from both of them. In Fig. 3 we show the membership of pages (orange for conspiracy and azure for science). In the first panel, memberships are given according to our categorization of pages (for further details refer to the Methods section). The second panel shows the page network with membership given by applying the multi-level modularity optimization algorithm [ 55 ]. In the third panel, membership is obtained by applying an algorithm that looks for the maximum modularity score [ 56 ].
Page Network. The membership of 73 pages as a) identified by means of their self-description, b) by applying the multi-level modularity optimization algorithm, and c) by looking at the maximum modularity score. Community detection algorithms based on modularity are good discriminants for community partitioning.
These findings indicates that connectivity patterns, in particular the modularity, between the two categories of pages differ. Since we are considering users’ likes on the pages’ posts, this aspect is pointing out a higher mobility of users of across pages of the conspiracy category.
Polarized users and their interaction patterns
mechanisms↗concepts↗concepts↗unhoused_relationssensesIn this section we focus on the users engagement across the different contents. Hence, we label users by means of a simple thresholding algorithm accounting for the percentage of likes on one or the other category. Notice that the choice of the like as a discriminant is grounded on the fact that generally such an action stands for a positive feedback to a post [ 50 ]. We consider a user to be polarized in a community when the number of his/her likes with respect to his/her total like activity on one category—scientific or conspiracy news—is higher than 95% (for further details about the algorithm refer to the Methods section). We identify 255,225 polarized users of scientific pages—i.e., resulting t be the 76,79% of users interacted on scientific pages) and 790,899 conspiracy polarized users—i.e., the 91,53% of users interacting with conspiracy pages in terms of liking. Users activity across pages is highly polarized. According to literature on opinion dynamics [ 37 ] in particular the one related to the Bounded Confidence Model (BCM) [ 51 ]—two nodes are able to influence each other only if the distance between their opinions is below a given distance—users consuming different and opposite information tend to form polarized clusters. The same hold If we look at commenting activity of polarized users inside and outside their community. In particular, those users that are polarized on conspiracy news tend to interact especially in their community both in terms of comments (99,08%) and likes. Users polarized in science tend to comment slightly more outside their community (90,29%). Results are summarized in Table 2 .
Activity of polarized users. Number of classified users for each category and their commenting activity on the category in which they are classified and on the opposite category. Users polarized on conspiracy pages tend to interact especially in their community both in terms of comments and likes. Users polarized in science are more active elsewhere.
Fig. 4 shows the CCDF for likes and comments of polarized users. unhoused_relationsDespite the very profound different nature of contents, consumption patterns are nearly the same both in terms of likes and comments. This finding indicates that very engaged users of different and clustered communities formed around different kind of narratives consume their preferred information in a similar way.
Consumption patterns of polarized users. Empirical complementary cumulative distribution function (CCDF) for likes and comments of polarized users.
unhoused_relationsAs a further investigation, we focus on the post where polarized users of both communities commented. Hence, we select the set of posts on which at least a polarized user of each of the two communities has commented. We find polarized users of communities debating on 7,751 posts (1,991 from science news and 5,760 from conspiracy news). The post at the interface, where the two communities discuss are mainly on the conspiracy side. As shown in Fig. 5 , polarized users of scientific news made 13,603 comments on post published by conspiracy news (9.71% of their total commenting activity), whereas polarized users of conspiracy news commented on scientific posts only 5,954 times (0.92% of their total commenting activity, i.e. roughly ten times less than polarized users of scientific news).
Activity and communities. Posts on which at least a member of each the two communities has commented. The number of posts is 7,751 (1,991 from scientific news and 5,760 from conspiracy news). Here we show the commenting activity in terms of polarized users on the two categories.
Response to false information
mechanisms↗concepts↗concepts↗unhoused_relationsOn online social networks, users discover and share information with their friends and through cascades of reshares information might reach a large number of individuals. Interesting is the popular case of Senator Cirenga’s [ 57 , 58 ] law proposing to fund policy makers with 134 billion of euros (10% of the Italian GDP) in case of defeat in the political competition. This was an intentional joke with an explicit mention to its satirical nature. The case of Senator Cirenga became popular within online political activists and used as an argumentation in political debates [ 47 ].
limitationsconcepts↗concepts↗concept_edgesOur analysis showed that users tend to aggregate around preferred contents shaping well defined groups having similar information consumption patterns. Our hypothesis is that the exposure to unsubstantiated claims (that are pervasive in online social media) might affect user selection criteria by increasing the attitude to interact with false information. Therefore, in this section we want to test how polarized users usually exposed to distinct narrative—one that can be veriefied (science news) and one that by definition is almost impossible to check—interact with posts that are deliberately false.
concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesTo do this we collected a set of troll posts—i.e. paradoxical imitations of conspiracy information sources. These posts are clearly unsubstantiated claims, like the undisclosed news that infinite energy has been finally discovered, or that a new lamp made of actinides (e.g. plutonium and uranium) might solve problems of energy gathering with less impact on the environment, or that the chemical analysis revealed that chem-trails contains sildenafil citratum (the active ingredient of Viagra™). Fig. 6 shows how polarized users of both categories interact with troll posts in terms of comments and likes. We find that polarized users of conspiracy pages are more active in liking and commenting on intentionally false claims.
Polarized users on false information. Percentage of comments and likes on intentional false memes posted by a satirical page from polarized users of the two categories.
Conclusions
Recently in [ 42 , 43 ] has been shown that unsubstantiated claims reverberate for a timespan comparable to the one of more verified information and that usual consumers of conspiracy theories are more prone to interact with them. Conspiracy theories find on the internet a natural medium for their diffusion and, not rarely, trigger collective counter-conspirational actions [ 59 , 60 ]. Narratives grounded on conspiracy theories tend to reduce the complexity of reality and are able to contain the uncertainty they generate [ 18 – 20 ]. contributionconcepts↗In this work we studied how users interact with information related to different (opposite) narratives on Facebook. Through a thresholding algorithm we label polarized users on the two categories of pages identifying well shaped communities. In particular, we measure commenting activity of polarized users on the opposite category, finding that polarized users of conspiracy news are more focused on posts of their community and their attention is more oriented to diffuse conspiracy contents. On the other hand, polarized users of scientific news are less committed in the diffusion and more prone to comment on conspiracy pages. A possible explanation for such a behavior is that the former want to diffuse what is neglected by main stream thinking, whereas the latter aims at inhibiting the diffusion of conspiracy news and proliferation of narratives based on unsubstantiated claims. Finally, we test how polarized users of both categories responded to the inoculation of 4,709 false claims produced by a parodistic page, finding polarized users of conspiracy pages to be the most active.
concepts↗concepts↗concept_edgesconcept_edgesThese results are coherent with the findings of [ 52 – 54 ] indicating the existence of a relationship between beliefs in conspiracy theories and the need for cognitive closure. Those who use a more heuristic approach when evaluating evidences to form their opinions are more likely to end up with an account more consistent with their existing system of beliefs. However, anti-conspiracy theorists may not only reject evidence that points toward a conspiracy theory account, but also spend cognitive resources for seeking out evidences to debunk conspiracy theories even when these are satirical imitation of false claims. These results open to new possibilities to understand popularity of information in online social media beyond simple structural metrics. Furthermore, we show that where unsubstantiated rumors are pervasive, false rumors might easy proliferate. Next envisioned steps for our research is to look at reactions of users to different kind of information according to a more detailed classification on contents.
Methods
Ethics Statement
limitationsconcepts↗The entire data collection process has been carried out exclusively through the Facebook Graph API [ 61 ], which is publicly available, and for the analysis (according to the specification settings of the API) we used only public available data (users with privacy restrictions are not included in the dataset). The pages from which we download data are public Facebook entities (can be accessed by anyone). User content contributing to such pages is also public unless the user’s privacy settings specify otherwise and in that case it is not available to us.
Data collection
concepts↗unhoused_relationsIn this study we address the effect of the usual exposure to diverse verifiable contents on the diffusion of false rumors. We identified two main categories of pages: conspiracy news—i.e. pages promoting contents neglected by main stream media—and science news. We defined the space of our investigation with the help of Facebook groups very active in debunking conspiracy theses ( Protesi di Protesi di Complotto , Che vuol dire reale , La menzogna diventa verita e passa alla storia ). We categorized page according to their contents and their self description.
Concerning conspiracy news, their self description is often claiming the mission to inform people about topics neglected by main stream media. Pages like Scienza di Confine , Lo Sai or CoscienzaSveglia promote heterogeneous contents ranging from aliens, chemtrails, geocentrism, up to the causal relation between vaccinations and homosexuality. We do not focus on the truth value of their information but rather on the possibility to verify their claims. Conversely, science news—e.g Scientificast , Italia unita per la scienza are active in diffusing posts about the most recent scientific advances. The selection of the source has been iterated several times and verified by all the authors. To our knowledge, the final dataset is the complete set of all scientific and conspiracist information sources active in the Italian Facebook scenario. In addition, we identify two pages posting satirical news with the aim of mocking usual rumors circulating on line by adding satirical contents.
method↗concepts↗concepts↗concept_edgesThe pages from which we downloaded data are public Facebook entities (can be accessed by virtually anyone). The resulting dataset is composed of 73 public pages divided in scientific and conspiracist news for which we downloaded all the posts (and their respective users interactions) over a timespan of 4 years (2010 to 2014).
The exact breakdown of the data is presented in Table 3 . The first category includes all pages diffusing conspiracy information—pages which disseminate controversial information, most often lacking supporting evidence and sometimes contradictory of the official news (i.e. conspiracy theories). The second category is that of scientific dissemination including scientific institutions and scientific press having the main mission to diffuse scientific knowledge.
Breakdown of Facebook dataset. The number of pages, posts, likes, comments, likers, and commenters for conspiracy and science news.
Preliminaries and Definitions
concepts↗concepts↗concept_edgesStatistical Tools . To characterize random variables, a main tool is the probability distribution function (PDF), which gives the probability that a random variable X assumes a value in the interval [ a , b ], i.e. P ( a ≤ X ≤ b ) = ∫ a b f ( x ) d x . The cumulative distribution function (CDF) is another important tool giving the probability that a random variable X is less than or equal to a given value x , i.e. F ( x ) = P ( X ≤ x ) = ∫ − ∞ x f ( y ) d y . In social sciences, an often occuring probability distribution function is the Pareto’s law f ( x ) ∼ x − γ , that is characterized by power law tails, i.e. by the occurrence of rare but relevant events. In fact, while f ( x ) → 0 for x → ∞ (i.e. high values of a random variable X are rare), the total probability of rare events is given by C ( x ) = P ( X > x ) = ∫ x ∞ f ( y ) d y , where x is a sufficiently large value. Notice that C ( x ) is the Complement to the CDF (CCDF), where complement indicates that C ( x ) = 1 − F ( x ). Hence, in order to better visualize the behavior of empirical heavy–tailed distributions, we recur to log–log plots of the CCDF.
concepts↗concepts↗concept_edgesBipartite Networks and Community Detection . We consider a bipartite network having as nodes users and affiliation the Facebook pages. A comment to a given information posted by a page determines a link between a user and a page. More formally, a bipartite graph is a triple G = ( A , B , E ) where A = { a i ∣ i = 1 … n A } and B = { b j ∣ j = 1 … n B } are two disjoint sets of vertices, and E ⊆ A × B is the set of edges—i.e. edges exist only between vertices of the two different sets A and B . The bipartite graph G is described by the matrix M defined as M i j = 1 i f a n e d g e e x i s t s b e t w e e n a i a n d b j 0 o t h e r w i s e
For our analysis we use the co-occurrence matrices C A = MM T and C B = M T M that count, respectively, the number of common neighbors between two vertices of A or B . C A is the weighted adjacency matrix of the co-occurrence graph C A with vertices on A . Each non-zero element of C A corresponds to an edge among vertices a i and a j with weight P i j A . concepts↗concepts↗unhoused_relationssensesclaim_relationsclaim_relationsTo test the community partitioning we use two well known community detection algorithms based on modularity [ 55 , 56 ]. The former algorithm is based on multi-level modularity optimization. Initially, each vertex is assigned to a community on its own. In every step, vertices are re-assigned to communities in a local, greedy way. Nodes are moved to the community in which they achieve the highest modularity. Differently, the latter algorithm looks for the maximum modularity score by considering all possible community structures in the network. We apply both algorithms to the bipartite projection on pages.
Labeling algorithm . The labeling algorithm can be described as thresholding strategy on the total number of users likes. Considering the total number of likes of a user L u on both posts P in categories S and C . Let l s and l c define the number of likes of a user u on P s or P c , respectively denoting posts from scientific and conspiracy pages. Then, we will have the total like activity of users on one category expressed as l s L u . Fixing a threshold θ we can discriminate users with enough activity on one category. More precisely, the condition for a user to be labeled as a polarized user in one category can be described as l s L u ∨ l c L u > θ . In Fig. 7 we show the number of polarized users as a function of θ . Both curves decrease with a comparable rate.
Polarized users and activity. Number of polarized users as a function of the thresholding value θ on the two categories.
List of pages
In this section are listed pages of our dataset. In Table 4 the list of scientific news and on Table 5 the list of conspiracy pages.
Scientific news sources. List of Facebook pages diffusing main stream scientific news and their url.
Conspiracy news sources. List of Facebook pages diffusing conspiracy news and their url.
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 13 of 13 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
This record holds no controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | Facebook consumption and community formation around mainstream scientific news, conspiracy news, and deliberately false satirical posts | object_of_study | the paper's words | |
| research_question | How do users consume opposing scientific and conspiracy narratives on Facebook, how do those narratives shape polarized communities, and how do the communities respond to deliberately false content? | research_question | the paper's words | |
| contribution | a four-year, 73-page Facebook analysis that links content categories to community structure, measures cross-community interaction, and tests whether polarized users engage with intentionally false parody | contribution | the paper's words | |
| claim_type | existence | claim_type | the paper's words | |
| phenomenon | polarized information communities and selective engagement with misinformation on online social media | phenomenon | the paper's words | |
| method | observational-study also in: observational-study | method | the paper's words | |
| mechanisms | mechanisms | mechanisms[0] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[1] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[2] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[3] | 0.99 | the tagger's words |
| limitations | the study is scoped to the Italian Facebook context and a hand-assembled atlas of scientific and conspiracy pages | limitations[0] | 0.99 | the tagger's words |
| limitations | the conspiracy category is based on page content and self-description; it is not a determination that every item is false | limitations[1] | 0.99 | the tagger's words |
| limitations | likes are generally treated as positive feedback, but ironic motivations cannot be detected | limitations[2] | 0.99 | the tagger's words |
| limitations | comments may be positive or negative, so comment counts measure attention or debate rather than endorsement | limitations[3] | 0.99 | the tagger's words |
| limitations | only publicly available users and content were accessible through the API; users with privacy restrictions are excluded | limitations[4] | 0.99 | the tagger's words |
| limitations | the exposure-to-unsubstantiated-claims account is explicitly posed as a hypothesis, while the dataset records observational interaction patterns | limitations[5] | 0.99 | the tagger's words |
| concepts | online social media also in: online social media | concepts[0] | 0.99 | the paper's words |
| concepts | shared beliefs, interests, worldviews and narratives | concepts[1] | 0.99 | the paper's words |
| concepts | collective intelligence also in: collective intelligence | concepts[2] | 0.99 | the paper's words |
| concepts | unsubstantiated rumors also in: unsubstantiated rumors | concepts[3] | 0.99 | the paper's words |
| concepts | conspiracy theories also in: conspiracy theories | concepts[4] | 0.99 | the paper's words |
| concepts | online social networks (OSN) also in: online social networks (OSN) | concepts[5] | 0.99 | the paper's words |
| concepts | main stream scientific news also in: main stream scientific news | concepts[6] | 0.99 | the paper's words |
| concepts | conspiracy news also in: conspiracy news | concepts[7] | 0.99 | the paper's words |
| concepts | distinct types of contents also in: distinct types of contents | concepts[8] | 0.99 | the paper's words |
| concepts | polarized communities also in: polarized communities | concepts[9] | 0.99 | the paper's words |
| concepts | troll information also in: troll information | concepts[10] | 0.99 | the paper's words |
| concepts | informational blends also in: informational blends | concepts[11] | 0.99 | the paper's words |
| concepts | biased beliefs also in: biased beliefs | concepts[12] | 0.99 | the paper's words |
| concepts | social reinforcement also in: social reinforcement | concepts[13] | 0.99 | the paper's words |
| concepts | trust an information someway consistent with their system of beliefs | concepts[14] | 0.99 | the paper's words |
| concepts | complexity of reality also in: complexity of reality | concepts[15] | 0.99 | the paper's words |
| concepts | climate of disengagement from mainstream society | concepts[16] | 0.99 | the paper's words |
| concepts | false rumors also in: false rumors | concepts[17] | 0.99 | the paper's words |
| concepts | false beliefs also in: false beliefs | concepts[18] | 0.99 | the paper's words |
| concepts | large heterogeneity of information also in: large heterogeneity of information | concepts[19] | 0.99 | the paper's words |
| concepts | aggregation of people around common interests, worldviews and narratives | concepts[20] | 0.99 | the paper's words |
| concepts | consumption patterns also in: consumption patterns | concepts[21] | 0.99 | the paper's words |
| concepts | a like also in: a like | concepts[22] | 0.99 | the paper's words |
| concepts | positive feedback also in: positive feedback | concepts[23] | 0.99 | the paper's words |
| concepts | a share also in: a share | concepts[24] | 0.99 | the paper's words |
| concepts | increase the visibility of a given information | concepts[25] | 0.99 | the paper's words |
| concepts | comment also in: comment | concepts[26] | 0.99 | the paper's words |
| concepts | online collective debates also in: online collective debates | concepts[27] | 0.99 | the paper's words |
| concepts | polarized users also in: polarized users | concepts[28] | 0.99 | the paper's words |
| concepts | 95% also in: 95% | concepts[29] | 0.99 | the paper's words |
| concepts | Bounded confidence model (BCM) also in: Bounded confidence model (BCM) | concepts[30] | 0.99 | the paper's words |
| concepts | isolated clusters also in: isolated clusters | concepts[31] | 0.99 | the paper's words |
| concepts | need for cognitive closure also in: need for cognitive closure | concepts[32] | 0.99 | the paper's words |
| concepts | heavy–tailed consumption patterns also in: heavy–tailed consumption patterns | concepts[33] | 0.99 | the paper's words |
| concepts | Pearson coefficient also in: Pearson coefficient | concepts[34] | 0.99 | the paper's words |
| concepts | information-based communities also in: information-based communities | concepts[35] | 0.99 | the paper's words |
| concepts | bipartite network of pages and users also in: bipartite network of pages and users | concepts[36] | 0.99 | the paper's words |
| concepts | modularity also in: modularity | concepts[37] | 0.99 | the paper's words |
| concepts | multi-level modularity optimization algorithm | concepts[38] | 0.99 | the paper's words |
| concepts | false information also in: false information | concepts[39] | 0.99 | the paper's words |
| concepts | unsubstantiated claims also in: unsubstantiated claims | concepts[40] | 0.99 | the paper's words |
| concepts | user selection criteria also in: user selection criteria | concepts[41] | 0.99 | the paper's words |
| concepts | cascades of reshares also in: cascades of reshares | concepts[42] | 0.99 | the paper's words |
| concepts | information might reach a large number of individuals | concepts[43] | 0.99 | the paper's words |
| concepts | polarized users of conspiracy pages also in: polarized users of conspiracy pages | concepts[44] | 0.99 | the paper's words |
| concepts | polarized users of scientific news also in: polarized users of scientific news | concepts[45] | 0.99 | the paper's words |
| concepts | heuristic approach also in: heuristic approach | concepts[46] | 0.99 | the paper's words |
| concepts | account more consistent with their existing system of beliefs | concepts[47] | 0.99 | the paper's words |
| concepts | Facebook Graph API also in: Facebook Graph API | concepts[48] | 0.99 | the paper's words |
| concepts | resulting dataset also in: resulting dataset | concepts[49] | 0.99 | the paper's words |
| concepts | 73 public pages also in: 73 public pages | concepts[50] | 0.99 | the paper's words |
| concepts | pages promoting contents neglected by main stream media | concepts[51] | 0.99 | the paper's words |
| concepts | troll posts also in: troll posts | concepts[52] | 0.99 | the paper's words |
| concepts | paradoxical imitations of conspiracy information sources | concepts[53] | 0.99 | the paper's words |
| concepts | bipartite graph also in: bipartite graph | concepts[54] | 0.99 | the paper's words |
| concepts | triple G also in: triple G | concepts[55] | 0.99 | the paper's words |
| concepts | Pareto’s law also in: Pareto’s law | concepts[56] | 0.99 | the paper's words |
| concepts | probability distribution function also in: probability distribution function | concepts[57] | 0.99 | the paper's words |
| unhoused_relations | cascades of reshares → information might reach a large number of individuals Through cascades of reshares, information might reach a large number of individuals. | unhoused_relations[0] | 0.99 | the tagger's words |
| unhoused_relations | conspiracy news → pages promoting contents neglected by main stream media Conspiracy news is introduced as pages promoting content said to be neglected by mainstream media. | unhoused_relations[1] | 0.99 | the tagger's words |
| unhoused_relations | troll posts → paradoxical imitations of conspiracy information sources Troll posts are introduced as paradoxical imitations of conspiracy information sources. | unhoused_relations[2] | 0.99 | the tagger's words |
| unhoused_relations | polarized users of conspiracy pages → polarized users of scientific news Conspiracy-polarized users are more active than science-polarized users on intentionally false claims. | unhoused_relations[3] | 0.99 | the tagger's words |
| unhoused_relations | a like → positive feedback A like generally stands for positive feedback to a post. | unhoused_relations[4] | 0.99 | the tagger's words |
| unhoused_relations | a share → increase the visibility of a given information A share expresses the user's will to increase information visibility. | unhoused_relations[5] | 0.99 | the tagger's words |
| unhoused_relations | comment → online collective debates Comments are the way online collective debate takes form around a post. | unhoused_relations[6] | 0.99 | the tagger's words |
| unhoused_relations | polarized users → 95% A user is labelled polarized when more than 95% of likes fall in one category. | unhoused_relations[7] | 0.99 | the tagger's words |
| unhoused_relations | information-based communities → modularity The study tests information-community partitions using modularity-based algorithms. | unhoused_relations[8] | 0.99 | the tagger's words |
| unhoused_relations | polarized users → consumption patterns Highly engaged users in distinct narrative communities consume preferred information in a similar way. | unhoused_relations[9] | 0.99 | the tagger's words |
| unhoused_relations | polarized users of scientific news → conspiracy news Science-polarized users comment across the community boundary roughly ten times more often than conspiracy-polarized users do. | unhoused_relations[10] | 0.99 | the tagger's words |
| senses | `conspiracy news` is a CATEGORY OF SOURCES AND VERIFIABILITY, not a truth verdict: the authors explicitly say they do not claim all conspiracy information is false | senses[0] | 0.99 | the tagger's words |
| senses | `polarized user` means a USER WHOSE LIKES EXCEED A 95% WITHIN-CATEGORY THRESHOLD; it is not an attitude survey or ideological self-report | senses[1] | 0.99 | the tagger's words |
| senses | `like`, `share`, and `comment` are treated differently: positive feedback, visibility intent, and debate respectively; comment valence can be positive or negative | senses[2] | 0.99 | the tagger's words |
| senses | `false information` in the response test means deliberately fabricated PARODY whose falsity is known by construction, not ordinary conspiracy posts | senses[3] | 0.99 | the tagger's words |
| senses | `community` is inferred from user-page connectivity and tested with two modularity-based partitioning algorithms rather than assigned from activity volume alone | senses[4] | 0.99 | the tagger's words |
| claim_relations | builds-on → Bounded Confidence Model (BCM) note: The paper explicitly uses BCM to interpret isolated polarization under opinion distance. | claim_relations[0] | 0.99 | the tagger's words |
| claim_relations | builds-on → Pearson coefficient note: The action-correlation analysis explicitly uses the Pearson coefficient. | claim_relations[1] | 0.99 | the tagger's words |
| claim_relations | builds-on → multi-level modularity optimization algorithm note: The page-network analysis explicitly applies this established algorithm. | claim_relations[2] | 0.99 | the tagger's words |
| claim_relations | builds-on → maximum-modularity community detection algorithm note: The analysis also explicitly applies an algorithm that searches for the maximum modularity score. | claim_relations[3] | 0.99 | the tagger's words |
| concept_edges | conspiracy news —contrasted-with→ main stream scientific news the relation itself is separately evidenced · strength of the relation: 1 · note: The two categories are explicitly introduced as distinct and opposite. · the relation stated: The study contrasts conspiracy news with mainstream scientific news as very distinct information narratives. also in: conspiracy news · main stream scientific news | concept_edges[0] | 0.99 | the tagger's words |
| concept_edges | conspiracy theories —causes→ complexity of reality direction on the measure: reduces · the relation itself is separately evidenced · strength of the relation: 0.7 · note: The phrase 'tend to reduce' is a hedged negative effect on complexity; the edge target is complexity itself and the relation preserves the reduction sign. · the relation stated: Conspiracy narratives tend to reduce the complexity of reality. also in: conspiracy theories · complexity of reality | concept_edges[1] | 0.99 | the tagger's words |
| concept_edges | conspiracy theories —causes→ climate of disengagement from mainstream society the relation itself is separately evidenced · strength of the relation: 0.9 · note: The paper flatly states an ability to create this climate. · the relation stated: Conspiracy narratives are able to create disengagement from mainstream society. | concept_edges[2] | 0.99 | the tagger's words |
| concept_edges | false rumors —causes→ false beliefs the relation itself is separately evidenced · strength of the relation: 0.95 · note: The causal phrase 'in turn create' is direct. · the relation stated: False rumors in turn create false beliefs. also in: false rumors · false beliefs | concept_edges[3] | 0.99 | the tagger's words |
| concept_edges | social reinforcement —causes→ trust an information someway consistent with their system of beliefs the relation itself is separately evidenced · strength of the relation: 0.75 · note: The 'more likely' wording is gradable rather than deterministic. · the relation stated: Social reinforcement makes people more likely to trust belief-consistent information.
Audit 2026-09-28: DOUBTFUL - read against its span and kept, but social reinforcement is DEFINED as this tendency ('i.e.'); causes is the wrong type. | concept_edges[4] | 0.99 | the tagger's words |
| concept_edges | large heterogeneity of information —causes→ aggregation of people around common interests, worldviews and narratives the relation itself is separately evidenced · strength of the relation: 0.9 · note: The paper uses the causal verb 'fostered'. · the relation stated: Information heterogeneity fostered aggregation around shared orientations. | concept_edges[5] | 0.99 | the tagger's words |
| concept_edges | distinct types of contents —causes→ polarized communities the relation itself is separately evidenced · strength of the relation: 0.95 · note: The abstract reports that communities emerge around and are shaped by content types. · the relation stated: Distinct content types shape and produce polarized communities.
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but polarized communities 'emerge around' distinct types of contents; causes is loose. also in: distinct types of contents · polarized communities | concept_edges[6] | 0.99 | the tagger's words |
| concept_edges | unsubstantiated claims —causes→ user selection criteria the relation itself is separately evidenced · strength of the relation: 0.5 — the record marks this weak · note: This is introduced as the study's hypothesis with 'might affect', not as a settled effect. · the relation stated: Exposure to unsubstantiated claims might affect users' content-selection criteria.
Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the authors' HYPOTHESIS is that exposure to unsubstantiated claims 'might affect' user selection criteria; causes is hedged. also in: unsubstantiated claims · user selection criteria | concept_edges[7] | 0.99 | the tagger's words |
| concept_edges | heuristic approach —causes→ account more consistent with their existing system of beliefs the relation itself is separately evidenced · strength of the relation: 0.75 · note: The conclusion says 'more likely', preserving the probabilistic hedge. · the relation stated: A heuristic evidence-evaluation approach makes a belief-consistent account more likely. | concept_edges[8] | 0.99 | the tagger's words |
| concept_edges | unsubstantiated rumors —causes→ false rumors the relation itself is separately evidenced · strength of the relation: 0.55 — the record marks this weak · note: The conclusion uses 'might', so this remains a weak causal possibility. · the relation stated: Where unsubstantiated rumors are pervasive, false rumors might proliferate.
Audit 2026-09-28: DOUBTFUL - read against its span and kept, but 'where unsubstantiated rumors are pervasive, false rumors MIGHT easily proliferate'; causes is hedged. also in: unsubstantiated rumors · false rumors | concept_edges[9] | 0.99 | the tagger's words |
| concept_edges | 73 public pages —part-of→ resulting dataset the relation itself is separately evidenced · strength of the relation: 1 · note: Categorical composition is explicit. · the relation stated: The resulting dataset is composed of 73 public pages. also in: 73 public pages · resulting dataset | concept_edges[10] | 0.99 | the tagger's words |
| concept_edges | bipartite graph —is-a→ triple G the relation itself is separately evidenced · strength of the relation: 1 · note: Direct formal class statement. · the relation stated: A bipartite graph is a triple G of two disjoint vertex sets and cross-set edges. also in: bipartite graph · triple G | concept_edges[11] | 0.99 | the tagger's words |
| concept_edges | Pareto’s law —is-a→ probability distribution function the relation itself is separately evidenced · strength of the relation: 1 · note: Direct class membership statement. · the relation stated: Pareto's law is a probability distribution function with power-law tails. also in: Pareto’s law · probability distribution function | concept_edges[12] | 0.99 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.