Investigating Fake and Reliable News Sources Using Complex Networks Analysis

Abstract

The rise of disinformation in the last years has shed light on the presence of bad actors that produce and spread misleading content every day. Therefore, looking at the characteristics of these actors has become crucial for gaining better knowledge of the phenomenon of disinformation to fight it. This study seeks to understand how these actors, meant here as unreliable news websites, differ from reliable ones. With this aim, we investigated some well-known fake and reliable news sources and their relationships, using a network growth model based on the overlap of their audience. Then, we peered into the news sites’ sub-networks and their structure, finding that unreliable news sources’ sub-networks are overall disassortative and have a low–medium clustering coefficient, indicative of a higher fragmentation. The k-core decomposition allowed us to find the coreness value for each node in the network, identifying the most connectedness site communities and revealing the structural organization of the network, where the unreliable websites tend to populate the inner shells. By analyzing WHOIS information, it also emerged that unreliable websites generally have a newer registration date and shorter-term registrations compared to reliable websites. The results on the political leaning of the news sources show extremist news sources of any political leaning are generally mostly responsible for producing and spreading disinformation.

1 Introduction

The use of new technologies and the growing number of alternative information sources—often unreliable—have dramatically changed how news is delivered, hence the reading habits of online users. This has led to reviewing and redefining not only people’s beliefs and perceptions of source credibility [1] but also the way people assimilate information faster and more automatically than ever.

The 2016 US elections and the recent SARS-CoV-2 pandemic have put a spotlight on the inappropriate use of some of these technologies to boost the production and dissemination of fake news and deceptive content across the World Wide Web (Web, for short). The increase in the number of new domains, often created by internal or foreign actors to promote false information [2] and undermine public opinion, has further contributed to the problem of information overload, also driven by the absence of content regulation on the Internet [3], which guarantees the basic prerequisite of democracy (freedom of thought, belief, opinion, and expression), and by the ease of the process of buying a domain name and building a website. Website builders, such as Wix [4], GoDaddy [5], and Wordpress [6], help make one’s voice heard by offering basic plans that make it easy, fast, and user-friendly and are of low-cost to create, host, and manage the content of a website or blog, giving the possibility, for individuals or companies, to earn some extra money by placing ads and then converting web-traffic into revenue [7]. However, a large number of websites and their activities on the Web are difficult to access and monitor.

Since the 90s, the study of the Web has attracted the attention of scientific communities in an attempt to better understand its topological structure. The model proposed by Albert et al. [8], for instance, illustrated the Web as a huge network whose nodes are the Web pages, and the links between the Web pages (hyperlinks) are the edges. [9] found that on the Web and in most real-world networks the number of links follows a power-law degree distribution (scale-free property) [10, 11], revealing that a minority of nodes are highly connected (hubs), whereas the vast majority have smaller degrees than average.

By modeling the websites and pages on the Web, Broder et al. [12] discovered that it has a bow-tie structure, with most accessible pages in a giant strongly connected component (GSCC) and pages that have not been linked yet to the GSCC in the IN or OUT component (the sides of the bow tie). A recent study [13] has shown the presence, on the Web, of local bow-tie structures, as most websites tend to focus on specific topics and content, being able to rely on traffic from loyal online users and frequent visitors.

Although the attention of researchers has shifted to the study of communities that populate social media platforms and the spread of disinformation within these environments in recent years [14–21], the Web can still represent an important resource for the study of online disinformation, allowing researchers to investigate the role of websites and their relationships that emerge into complex social structures, identifying communities, meant here as groups of websites [22] that are more densely connected than others (sparse connections) and share similar features.

The identification of such communities within the Web can, therefore, allow the detection of websites spreading misleading information and fabricated news, using the “friend of a friend” mechanism proposed by [23], which states that if two nodes (e.g., websites 1 and 2) are strongly linked to another one (e.g., website 3), then with very high probability they are strongly linked to each other (triadic closure) [24].

Based on the above, this study focuses on websites’ similarities to examine groups of websites sharing similar characteristics such as audience overlap or WHOIS information (e.g., registration/expiration date) to understand how to detect the increasing number of groups of websites that may spread false content.

In particular, this study focuses on some well-known international unreliable websites, that is, websites that have published or shared misleading content across the Web over the past years and mainstream media outlets. For each selected website, information on audience overlap, that is, a Search Engine Optimization (SEO) metric that provides insights on the overlap of audience and topics across analyzed websites, is extracted in order to build sub-networks by adding competitors as nodes and connect them based on this metric. By combining all the sub-networks, we derived a full network of approximately 12,200 nodes. As real-world systems have distinct topologies, networks and sub-networks’ structural properties, such as degree size, clustering coefficient, cliques, and degree-assortativity, are analyzed to get valuable insights on website relationships, especially among those that spread (fake) news.

2 Materials and Methods

2.1 Data Collection

We collected a list of unreliable websites due to the production of news created to deliberately misinform or deceive readers. The selection of these websites was based on the blacklists provided by international fact-checking websites (PolitiFact.com [27], poynter.org [28]) and the well-known reputable websites (csbnews.com [29]). Along with this list, we have a list of traditional, free, or least biased news sources which generally deliver reliable information supported by facts [20]. The list of unreliable and reliable news websites used for this study for building the sub-networks and then the full network are shown in Table 1.

Due to the huge number of unreliable websites that are registered every day worldwide, website selection was performed on some of the most frequently reported untrustworthy websites by well-known fact-checking websites.

For each website included in the original list (Table 1), we extracted up to five competitor websites based on audience overlap (AO) to get a representative sample of similar websites (competitors) for each website in our list. Specifically, the AO score indicates the similarity level between competitors and an analyzed website (target). Competitor analysis can provide valuable insights on potential competitors that could offer products or services (including news production and consumption) targeting the same audience as a particular target website (market segmentation). Information on AO is provided by external competitor analysis tools (e.g., Alexa or SimilarWeb) within SEO strategy.

Figure 1 illustrates the schematic iteration process to generate the final dataset comprising the initial list of reliable or unreliable news sources and their competitors. For each competitor, its audience overlap score is also collected. As shown in Figure 1 (dashed blue line box), once getting the first list of competitors of URL1 (iteration 1), the process runs again, now considering the list of competitors as target websites and collecting information on their competitors, extracting the AO scores. This process is repeated up to six iterations. A stopping criterion was set due to the exponential growth in the amount of data during the collection phase. We stopped the process after six iterations due to the “six-degree” phenomenon, which applies to many kinds of networks, including the social ones, and which should ensure that other websites are generally reached through an average of six websites [30–32]. As recent analyses on social networks have found that the average separation in a Facebook friend graph is less than four degrees [33, 34], this criterion should be enough to guarantee the distance distribution of websites, in a similar way to social networks, and a good sample of data.

FIGURE 1

Once getting all the information about competitors, a result dataset is created, removing any duplicate information. It is interesting to note that two or more websites may be competitors and present in their respective lists. However, due to the decreasing order of the overlap score and the limited number of competitors provided by SEO tools (generally up to 5), some websites may not be mutually present in the lists due to their higher similarity with other websites listed at the top. In order to not lose this information, the relationships, if any, between any two similar websites were considered reciprocal. Data was collected between July 2021 and August 2021.

The use of network analysis on these data allows us to represent nodes’ attributes and relationships in order to identify properties of the interactions that occur between the websites in the initial list, their competitors, and the competitors of their competitors.

For this purpose, we consider a graph, defined as G≔{V, E}, where V is the set of vertices, that is, websites (e.g., websites or blogs) within this context, and E≔{(i, j)|i, j ∈ V, i ≠ j, E ⊆ V × V} is the set of all pairs of distinct vertices, called edges, representing a relationship based on audience overlap between two websites i and j.

Therefore, the process illustrated in Figure 2 consists of adding, at each iteration, new nodes that connect to the existing nodes in the sub-network if they exhibit an audience overlap. We model this process as follows: let C be the set of cascades containing numbers of cascades as C = {c}. Each snapshot of cascade c at iteration n is described by a sub-graph , where V_c is a subset of vertices in V that have contributed to the cascade c at iteration n, and an edge denotes the relationship between i_c and j_c. After each iteration n, the graph G(n) is then updated with new vertices. Therefore, the growth size is defined as the increment of the size of cascade c after a given iteration Δn, and it is denoted as .

FIGURE 2

By applying the process shown in Figure 2 to each website in Table 1, we built several sub-networks, one for each website in the list. From the union of all these sub-networks, we built the final full network.

2.2 Data Labeling

The above assessments help fix discrepancies in labeling criteria (which may impact the analysis) and improve label quality against final manual labeling. The labeling phase was performed in December 2021.

Throughout this paper, the term “reliable” will be used to refer to websites within the categories “True” and “Mostly True,” whereas the term “unreliable” will be used to refer to websites within the categories “False” and “Mostly False.” These two categories also include the websites listed in Table 1.

The labels assigned are mutually exclusive. According to the scope of this work, priority was given to labels from fact-checking assessments rather than the scam ones. In fact, although all websites in the final list (about 12,200 distinct websites) have scam labels, only some are news websites. Accordingly, we assigned website labels to news (False/True) from fact-checking websites where present or performed a manual label assignment to identify news websites based on their content (Mostly False/True). As discussed above, we performed further sub-classification within the “Neutral” class. The distribution of websites inside the aforementioned macro-categories is as follows: 9,976 websites in the “Neutral” category; 509 websites in the “Mostly False” category; 489 websites in the “False” category; 134 websites in the “Mostly True” category; 254 websites in the “True” category; and 850 websites in “Missing data” category.

3 Results

3.1 Network Analysis

We built undirected, unweighted sub-networks, where each node represents a unique website, and an undirected link is added between two nodes whenever a website has an overlap of the audience with another website. Isolated nodes correspond to websites whose audience is too small to be detected. Once all data are collected for each website in the list and sub-networks are built, they are combined into one full network.

Figure 3 illustrates the full network formed by all the websites listed in Table 1 and their competitors, built by following the process described in Section 2.1. There are 12,107 non-isolated nodes and 18,161 links. Colors are assigned to nodes based on the macro-categories discussed in Section 2.2. Networks are generated using Graphistry, a GPU-accelerated platform that allows users to investigate more quickly and easily big networks and Python.

FIGURE 3

Properties are assessed on the full network and each sub-network, at both the network (global) and node (local) level, using NetworkX, a Python package for the creation, manipulation, and exploration of complex networks. Global network properties include the number of nodes and links (both for individual and full networks), the number of connected components, degree, network density, and assortativity. Local network properties include node’s degree, average neighbor degree, and clustering coefficient. We selected these features to show that, even by analyzing simple properties, it is possible to distinguish websites, in particular news ones.

Table 2 reports summary statistics of the full network. As shown in Figure 3, the full network is not completely connected, consisting of three disjoint connected components, with sizes of 11,772, 376, and 9. The dominant connected component (giant component) of the network holds a large fraction of the total number of nodes (11,772) and links. The second large component (376 websites) is mostly Italian. It was generated by collecting audience overlap data from byoblu.com (Table 1), an Italian website known for posting misleading and conspiracy theory content.

Assortativity and Clustering Coefficient

In order to determine homogeneity or heterogeneity of sub-networks, the assortativity measure is calculated for each sub-network created starting from each website in Table 1, after running the six-iteration process described in Section 2.1. The results in Table 3 show strong evidence for positive assortativity for websites within the category “Reliable,” except for www.ap.org; the assortative coefficient of the sub-networks of unreliable websites is negative overall, except for the values obtained from the sub-networks of clashdaily.com and conservativedailypost.com, respectively. The mean value and the standard error of the mean of the assortativity values of the sub-networks are, respectively, as follows:

Similarly, for the clustering coefficients,

An example of the sub-network structure of reliable and unreliable websites is shown in Figure 4.

FIGURE 4

The results listed in Table 3 above denote that communities of reliable news websites tend to be more clustered, whereas the sub-networks of unreliable news websites have a higher fragmentation (Figure 4).

k-Core Decomposition

To identify particular subsets of the full network (k-cores) [38], we filtered the nodes according to their label, mostly focusing on those that fell into the “Reliable” and “Unreliable” categories and on their nearest neighbors, regardless of the label of the latter. The k-core is then obtained by decomposing the network via recursive removal of least connected nodes (Figure 5), namely, those with a degree smaller than k, until the degree of all remaining nodes is larger than or equal to k [39]. Figure 6A shows the nested structure of a network of k-cores, consisting of a series of concentric “shells” from the outermost (periphery) at k_s = 1—which includes all the network—to the innermost—which corresponds to the maximum k-core, at ( in this case). The network decomposition has the advantage of reducing computation time, effectively providing information on the significance of the network’s nodes and community structures [40] by visualizing the central cores of the network. The removal of the noise caused by the bridge edges indeed allows the network to be divided into smaller components, improving the quality of the communities obtained and simplifying the structure of the topology of the remaining network. However, a k-core does not necessarily induce a connected network, as shown in Figure 6.

FIGURE 5

FIGURE 6

When we applied the k-core algorithm [41], we were able to identify several cores at different k values that hold across several overlapping communities. The denser core, made up of nodes with the highest coreness, is at k = 5 (Figure 6B). The 5-core has a size of 62 websites across four substructures and reveals a big community of fake news, conspiracy, and propaganda websites. It also highlights the role of fact-checking websites as bridges between the groups of reliable and unreliable websites.

The unreliable websites tend to populate the inner k-shells, whereas reliable websites generally concentrate more on the outer k-shells. This result hints that unreliable websites could be more dependent on the survival of many reliable websites than vice versa.

3.2 Political Bias: Reliable Versus Unreliable News Sources

We assigned a political bias label only to the 1,240 distinct news sources [18, 43] identified in our dataset within reliable (“True” or “Mostly True”) or unreliable (“False” or “Mostly False”) categories. We derived the labels using the classification provided by MediaBias/FactCheck (MBFC) [43], an independent online media outlet that provides information on news sources’ media bias and content reliability rating the sources using the U.S. political spectrum: Extreme Left, Left, Left-centre, Least biased, Right-centre, Right, Far Right, and Extreme Right. Along this Left-Right scale (Figure 7), other labels were also assigned to our data (Table 4), according to the MBFC scale: Conspiracy-Pseudoscience, Satire, Fake News, Source pending review (i.e., websites under review in MBFC at the time of the analysis, performed in January 2022), and Not Available (i.e., websites with no information available).

FIGURE 7

Based on labels provided by MBFC, we see in Figure 7 that the number of unreliable (“False” or “Mostly False”) news sources (circa 8%) which fall on the right-wing extremism is larger than the number of left extremist news sources. Regardless of the percentages, their results make sense and align well with the intuition that disinformation is politically charged and that extreme political views can produce biased information. News sources that overall exhibit a left or least bias tend to be more trustworthy than those extremely biased or have a moderate-to-strong right bias [45].

We set the alpha level (α) at 0.05. The result indicates a p-value equal to 2.013e⁻⁴²: this means that there is a strong positive and significant correlation between the two variables compared to the null hypothesis.

3.3 Domain Registration/Expiration Date

FIGURE 8

The domain registration date distribution of the unreliable set of data (box plot on the left in Figure 8) exhibits a clear negative skewness versus the distribution of domain registration dates of the reliable data, which instead exhibits a positive skewness of the distribution. This aligns with the intuition that fake news or misleading content is published or shared more likely by newer websites [46]. Although disinformation is not a recent problem, certainly the new technological tools and their easy accessibility have led, in recent years, to the amplification of this problem, making it more challenging.

Domains registered for a short period are often favored by bad actors to spread disinformation (Figure 9). Websites used for illegal or malicious purposes (including phishing, malware, and scam) are generally registered for a shorter registration/renewal period than websites created for legitimate purposes, or they are no longer available because they were archived or suspended for violation of terms of services. This can be explained as once identified as malicious and reported, these websites are seized or shut down. An example is provided by piracy websites, for example, torrent and streaming websites, or direct download platforms (Figure 9B). Many piracy websites go offline after a while, or they are shut down due to copyright violations and illegal file-sharing: therefore, owners of such domains usually do not register a domain for longer than a couple of years.

FIGURE 9

4 Discussion

Fake news has always existed. The Trojan horse, used by the Ancient Greeks during the Trojan War, is probably one of the first and most well-known examples of deception. In the last century, specifically through the years of Nazism and Fascism, censorship and propaganda were largely used for political purposes, aiding the one-party (e.g., Fascism party, National Socialist parties) in establishing their systems, supporting and promoting their ideology [47], and playing upon people’s fears and anxiety as well as upon their emotions and prejudices [48–50].

Undoubtedly, what has changed throughout the centuries is the way and the speed with which information is produced, disseminated, and consumed [51, 52]. The Web and new technologies and social platforms have made the world interconnected and helped information spread across the world. Accordingly, the Web represents an ideal place to “hide” disinformation in order to influence public opinion, damage reputation by disseminating lies, promote propaganda, and interfere in political elections [53]. Therefore, it is crucial to timely identify “bad actors”, that is, sources (humans or bots), which spread false content in order to fight online disinformation.

This study aimed to investigate the relationships between websites spreading deceptive content across the Web, trying to characterize them by comparing these websites with those deemed reliable. Therefore, we used a multidisciplinary approach to more easily detect and analyze the communities of unreliable websites, if any. Specifically, we extracted AO data, using SEO tools, and WHOIS information. Although this type of information has been extensively researched separately (SEO data mainly in marketing strategies, WHOIS data mainly for CyberSecurity activities), this study represents the first attempt to combine and explore it within the context of disinformation. The results seem to be promising: in fact, from an initial list of 34 websites, 25 of which were deemed unreliable, using the AO information of each website, we could identify approximately 880 websites that publish and/or share misinformation and are linked to those initially selected, directly or through other websites.

In order to investigate the relationships between websites, we used the Complex Networks theory, focusing not only on the full network but also on the sub-networks (built starting from the initial list of websites we selected) that created it. Although the sub-networks analysis is unusual, it made sense in this context. In fact, by definition, we have built the sub-networks starting from a website, reliable or unreliable, and this allowed us to obtain information about each sub-network and its structure.

As illustrated in Section 3, from a network perspective, the analysis and comparison of these sub-networks have highlighted important properties as being able to characterize—therefore distinguish—the websites considered reliable from those considered unreliable. In particular, the assortativity measure, generally analyzed in social networks [54], has shown the presence of a strong tendency for reliable news websites to be connected to each other [55].

One possible explanation for this result may be as follows: the sub-networks of reliable websites tend to be assortative because their audience often prefer to visit websites that are, or have links to, other websites that are similar to them. Moreover, sub-network clustering coefficient values for reliable news websites suggest that there are tightly connected communities in which most of the website’s competitors are themselves competitors (Figure 4A). Therefore, most audience concentrates only on a few websites, mostly the same, as also revealed by the sub-networks’ growths illustrated in Figure 10. As shown in Figure 10, it appears that both population sizes slowly grow up to the fourth iteration; then, the population associated with the unreliable websites jumps up at the fifth iteration, continuing to grow faster than that one of the reliable websites. However, a few exceptions were also identified, especially when some unreliable websites did not have many competitors due to the low volume of traffic and/or poor visibility.

FIGURE 10

The fast growth of the number of nodes within the sub-networks of unreliable websites can also be explained by looking at the example provided in Figure 10, where the sub-network built from the website react365.com exhibits a high fragmentation. This is very common in sub-networks built starting from unreliable websites, as also highlighted by their disassortative tendency, with highly connected nodes linked with poorly connected nodes. This tendency might be explained by considering the strategies adopted by fake and unreliable websites to spread disinformation, also looking at their audience’s behavior. It may happen in fact that mirror websites or sub-domains are created (“divide et impera” strategy; it is also employed to disrupt unity and cohesion in public opinion) to avoid detection tools and continue to spread content online to seek a wider and more loyal audience, then a greater possibility for disinformation. A well-known example of this is news-front.info (https://securingdemocracy.gmfus.org/russias-affront-on-the-news-how-newsfronts-persistence-past-social-media-bans-demonstrates-the-need-for-vigilance/).

Figure 4 illustrates an interesting result about audience Web searching behavior when we analyze the two types of sub-networks. It is possible to note that the audience of unreliable websites also consults fraudulent websites (e.g., generators of false documents/ID). This result might reveal possible underlying suspicious activities associated with online users from unreliable websites [56].

By analyzing the domain registration length, meant as the time period between the registration and the expiration dates, of websites included within the “Reliable” or “Unreliable” category, we found that websites used for malicious purposes are generally registered for a shorter period compared to websites created for legitimate purposes (Figure 9A). In terms of SEO strategies, bad actors may also be more interested in buying expired domain names or use mirror websites [61] to disseminate false information.

The ease with which websites can be created and managed without big expense or effort has therefore contributed to the problem of online disinformation. Furthermore, similar to what happens with piracy websites [62] and with Dark Web marketplaces [63], one might expect that closing a fake news website would make a minimal and short-lived difference in the amount of fake content consumption, as this would lead users to migrate to other websites.

The political bias of news sources also plays a key role in disinformation’s spread, as shown in Figure 7. Our findings align with results got from other research works [45, 64, 65], confirming that fake news and misleading content are published and/or shared more likely by people on the extreme right-wing than people on the left-wing. This can be explained by the fact that conservatives generally have higher vulnerability to political misperceptions and lower trust in media than liberals [66–68].

5 Conclusion

Infodemic has become a critical issue for modern society due to new technologies and social platforms that have made it easier to generate and disseminate information across the Web by internal and/or foreign actors that can create new fake accounts or websites or change the existing ones. Timely identification of the bad actors that spread false content has become a crucial element in fighting online disinformation in the early stages.

This research work can be seen as a first step toward the identification of disinformation spreaders through a multidisciplinary approach that combines the use of audience overlap, a well-known metric in marketing strategies for Search Engine Optimization, and the use of Complex Networks to visualize and analyze the relationships among websites via browsing behavior of online users to discover hidden relationships between websites.

The interplay between users’ browsing behavior—which represents a digital fingerprint—and disinformation mechanisms is a still unexplored research direction that may shed light on the website communities, which form and emerge while users navigate the Web, by analyzing SEO features and WHOIS data.

Finally, future research might include studying how both site networks and relationships among websites evolve over time, analyzing the spread of information across the Web (via Web Search Engines) and/or on social media (e.g., Twitter and Facebook).

References

• 1.

  WestermanDSpencePRVan Der HeideB. Social media as Information Source: Recency of Updates and Credibility of Information. J Comput-mediat Comm (2014) 19:171–83. 10.1111/jcc4.12041

  • CrossRef
  • Google Scholar

• 2.

  Justicegov. Justicegov (2020). Available from: www.justice.gov (Accessed January 22, 2022).

  • Google Scholar

• 3.

  RodriguesUMXuJ. Regulation of Covid-19 Fake News Infodemic in china and india. Media Int Aust (2020) 177:125–31. 10.1177/1329878X20948202

  • CrossRef
  • Google Scholar

• 4.

  Wix. Wix (2006). Available from: https://www.wix.com (Accessed February 21, 2022).

  • Google Scholar

• 5.

  GoDaddy. GoDaddy (1997). Available from: https://www.godaddy.com (Accessed February 21, 2022).

  • Google Scholar

• 6.

  Wordpress. Wordpress (2003). Available from: https://www.wordpress.com (Accessed February 21, 2022).

  • Google Scholar

• 7.

  Sitelike. Sitelike (2021). Available from: https://www.sitelike.org (Accessed August 20, 2021).

  • Google Scholar

• 8.

  AlbertRJeongHBarabásiA-L. Diameter of the World-wide Web. Nature (1999) 401:130–1. 10.1038/43601

  • CrossRef
  • Google Scholar

• 9.

  Barabási A.-L, Albert R. Emergence of Scaling in Random Networks. Science (1999) 286:509-12. 10.1126/science.286.5439.509

• 10.

  NewmanMBarabásiALWattsDJ. The Structure and Dynamics of Networks. Princeton, NJ, USA: Princeton University Press (2006).

  • Google Scholar

• 11.

  AdamicLAHubermanBABarabasiALAlbertRJeongHBianconiG. Power-law Distribution of the World Wide Web. Science (2000) 287:2115. 10.1126/science.287.5461.2115a

  • CrossRef
  • Google Scholar

• 12.

  BroderAKumarRMaghoulFRaghavanPRajagopalanSStataRet alGraph Structure in the Web. Computer Networks (2000) 33:309–20. 10.1016/s1389-1286(00)00083-9

  • CrossRef
  • Google Scholar

• 13.

  FujitaYKichikawaYFujiwaraYSoumaWIyetomiH. Local bow-tie Structure of the Web. Appl Netw Sci (2019) 4. 10.1007/s41109-019-0127-2

  • CrossRef
  • Google Scholar

• 14.

  RuffoGSemeraroAGiachanouARossoP. Surveying the Research on Fake News in Social media: A Tale of Networks and Language (2021).

  • Google Scholar

• 15.

  Del VicarioMBessiAZolloFPetroniFScalaACaldarelliGet alThe Spreading of Misinformation Online. Proc Natl Acad Sci U.S.A (2016) 113:554–9. 10.1073/pnas.1517441113

  • CrossRef
  • Google Scholar

• 16.

  StellaMFerraraEDe DomenicoM. Bots Increase Exposure to Negative and Inflammatory Content in Online Social Systems. Proc Natl Acad Sci U.S.A (2018) 115:12435–40. 10.1073/pnas.1803470115

  • CrossRef
  • Google Scholar

• 17.

  ShaoCCiampagliaGLVarolOYangK-CFlamminiAMenczerF. The Spread of Low-Credibility Content by Social Bots. Nat Commun (2018) 9. 10.1038/s41467-018-06930-7

  • CrossRef
  • Google Scholar

• 18.

  CinelliMQuattrociocchiWGaleazziAValensiseCMBrugnoliESchmidtALet alThe Covid-19 Social media Infodemic. Sci Rep (2020) 10:16598. 10.1038/s41598-020-73510-5

  • CrossRef
  • Google Scholar

• 19.

  GallottiRValleFCastaldoNSaccoPDe DomenicoM. Assessing the Risks of 'infodemics' in Response to COVID-19 Epidemics. Nat Hum Behav (2020) 4:1285–93. 10.1038/s41562-020-00994-6

  • CrossRef
  • Google Scholar

• 20.

  PierriFPiccardiCCeriS. Topology Comparison of Twitter Diffusion Networks Effectively Reveals Misleading Information. Sci Rep (2020) 10. 10.1038/s41598-020-58166-5

  • CrossRef
  • Google Scholar

• 21.

  CaldarelliGDe NicolaRPetrocchiMPratelliMSaraccoF. Flow of Online Misinformation during the Peak of the Covid-19 Pandemic in italy. EPJ Data Sci (2021) 10. 10.1140/epjds/s13688-021-00289-4

  • CrossRef
  • Google Scholar

• 22.

  BurgessMAdarECafarellaM. Link-Prediction Enhanced Consensus Clustering for Complex Networks. PLOS ONE (2016) 11:e0153384–23. 10.1371/journal.pone.0153384

  • CrossRef
  • Google Scholar

• 23.

  GranovetterMS. The Strength of Weak Ties. Am J Sociol (1973) 78:1360–80. 10.1086/225469

  • CrossRef
  • Google Scholar

• 24.

  BianconiGDarstRKIacovacciJFortunatoS. Triadic Closure as a Basic Generating Mechanism of Communities in Complex Networks. Phys Rev E (2014) 90:042806. 10.1103/PhysRevE.90.042806

  • CrossRef
  • Google Scholar

• 25.

  Alexa. Alexa (1996). Available from: https://www.alexa.com/ (Accessed February 21, 2022).

  • Google Scholar

• 26.

  Similarweb. Similarweb (2007). Available from: https://www.similarweb.com (Accessed February 21, 2022).

  • Google Scholar

• 27.

  PolitiFact. PolitiFact (2007). Available from: https://www.politifact.com (Accessed February 21, 2022).

  • Google Scholar

• 28.

  Poynter. Poynter (1975). Available from: https://www.poynter.org (Accessed February 21, 2022).

  • Google Scholar

• 29.

  CBSNews. CBSNews (1927). Available from: https://www.cbsnews.com (Accessed February 21, 2022).

  • Google Scholar

• 30.

  MilgramS. The Small World Problem. Psychol Today (1967) 2:60–7. 10.1037/e400002009-005

  • CrossRef
  • Google Scholar

• 31.

  TraversJMilgramS. An Experimental Study of the Small World Problem. Sociometry (1969) 32:425–43. 10.2307/2786545

  • CrossRef
  • Google Scholar

• 32.

  BarabásiA-L. Network Science. Phil Trans R Soc A (2013) 371:20120375. 10.1098/rsta.2012.0375

  • CrossRef
  • Google Scholar

• 33.

  DaraghmiEYYuanS-M. We Are So Close, Less Than 4 Degrees Separating You and Me!Comput Hum Behav (2014) 30:273–85. 10.1016/j.chb.2013.09.014

  • CrossRef
  • Google Scholar

• 34.

  Facebook. Three and a Half Degrees of Separation (2016). Available from: https://research.fb.com (Accessed August 20, 2021).

  • Google Scholar

• 35.

  ScamAdvisercom. ScamAdvisercom (2012). Available from: https://www.scamadviser.com (Accessed February 21, 2022).

  • Google Scholar

• 36.

  SamarasingheNMannanM. On Cloaking Behaviors of Malicious Websites. Comput Security (2021) 101:102114. 10.1016/j.cose.2020.102114

  • CrossRef
  • Google Scholar

• 37.

  MontesFJaramilloAMMeiselJDDiaz-GuileraAValdiviaJASarmientoOLet alBenchmarking Seeding Strategies for Spreading Processes in Social Networks: an Interplay between Influencers, Topologies and Sizes. Sci Rep (2020) 10:3666. 10.1038/s41598-020-60239-4

  • CrossRef
  • Google Scholar

• 38.

  MontresorADe PellegriniFMiorandiD. Distributed K-Core Decomposition. IEEE Trans Parallel Distrib Syst (2013) 24:288–300. 10.1109/TPDS.2012.124

  • CrossRef
  • Google Scholar

• 39.

  Alvarez-HamelinJDall’AstaLBarratAVespignaniA. K-core Decomposition: A Tool for the Visualization of Large Scale Networks. Adv Neural Inf Process Syst (2005) 18.

  • Google Scholar

• 40.

  MalvestioICardilloAMasudaN. Interplay between $$k$$-Core and Community Structure in Complex Networks. Sci Rep (2020) 10. 10.1038/s41598-020-71426-8

  • CrossRef
  • Google Scholar

• 41.

  BatageljVZaveršnikM. An O(m) Algorithm for Cores Decomposition of Networks. CoRR cs.DS/0310049 (2003).

  • Google Scholar

• 42.

  Mediamattersorg. Mediamattersorg (2018). Available from: https://www.mediamatters.org (Accessed January 22, 2022).

  • Google Scholar

• 43.

  PennycookGRandDG. Fighting Misinformation on Social media Using Crowdsourced Judgments of News Source Quality. Proc Natl Acad Sci U.S.A (2019) 116:2521–6. 10.1073/pnas.1806781116

  • CrossRef
  • Google Scholar

• 44.

  Mediabiasfactcheckcom. MediaBias/FactCheck (2015). Available from: https://mediabiasfactcheck.com (Accessed February 20, 2022).

  • Google Scholar

• 45.

  BovetAMakseHA. Influence of Fake News in Twitter during the 2016 Us Presidential Election. Nat Commun (2019) 10. 10.1038/s41467-018-07761-2

  • CrossRef
  • Google Scholar

• 46.

  MazzeoVRapisardaAGiuffridaG. Detection of Fake News on Covid-19 on Web Search Engines. Front Phys (2021) 9:685730. 10.3389/fphy.2021.685730

  • CrossRef
  • Google Scholar

• 47.

  YourmanJ. Propaganda Techniques within Nazi germany. J Educ Sociol (1939) 13. 10.2307/2262307

  • CrossRef
  • Google Scholar

• 48.

  MartelCPennycookGRandDG. Reliance on Emotion Promotes Belief in Fake News. Cogn Res (2020) 5:47. 10.1186/s41235-020-00252-3

  • CrossRef
  • Google Scholar

• 49.

  SalviCIannelloPCancerAMcClayMDunsmoorJAntoniettiA. Going Viral: How Fear, Socio-Cognitive Polarization and Problem-Solving Influence Fake News Detection and Proliferation during Covid-19 Pandemic. Front Commun (2021) 5. 10.3389/fcomm.2020.562588

  • CrossRef
  • Google Scholar

• 50.

  EckerUKHLewandowskySCookJSchmidPFazioLKBrashierNet alThe Psychological Drivers of Misinformation Belief and its Resistance to Correction. Nat Rev Psychol (2022) 1:13–29. 10.1038/s44159-021-00006-y

  • CrossRef
  • Google Scholar

• 51.

  VosoughiSRoyDAralS. The Spread of True and False News Online. Science (2018) 359:1146–51. 10.1126/science.aap9559

  • CrossRef
  • Google Scholar

• 52.

  TalwarSDhirASinghDVirkGSSaloJ. Sharing of Fake News on Social media: Application of the Honeycomb Framework and the Third-Person Effect Hypothesis. J Retailing Consumer Serv (2020) 57:102197. 10.1016/j.jretconser.2020.102197

  • CrossRef
  • Google Scholar

• 53.

  Justicegov. United states Seizes Domain Names Used by iran’s Islamic Revolutionary Guard Corps (2020). figshare. Available from: www.justice.gov (Accessed February 20, 2022).

  • Google Scholar

• 54.

  MuldersDde BodtCBjellandJPentlandASVerleysenMde MontjoyeYA. Improving Individual Predictions Using Social Networks Assortativity. In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) (2017). p. 1–8. 10.1109/wsom.2017.8020023

  • CrossRef
  • Google Scholar

• 55.

  CeroIWitteTK. Assortativity of Suicide-Related Posting on Social media. Am Psychol (2019) 75:365–79. 10.1037/amp0000477

  • CrossRef
  • Google Scholar

• 56.

  WangWShirleyK. Breaking Bad: Detecting Malicious Domains Using Word Segmentation. In: IEEE Web 2.0 Security and Privacy Workshop (W2SP) (2015).

  • Google Scholar

• 57.

  SeidmanSB. Network Structure and Minimum Degree. Social Networks (1983) 5:269–87. 10.1016/0378-8733(83)90028-X

  • CrossRef
  • Google Scholar

• 58.

  KongY-XShiG-YWuR-JZhangY-C. K-Core: Theories and Applications. Phys Rep (2019) 832:1–32. 10.1016/j.physrep.2019.10.004

  • CrossRef
  • Google Scholar

• 59.

  EmersonAIAndrewsSAhmedIAzisTKMalekJA. K-core Decomposition of a Protein Domain Co-occurrence Network Reveals Lower Cancer Mutation Rates for interior Cores. J Clin Bioinforma (2015) 5:1. 10.1186/s13336-015-0016-6

  • CrossRef
  • Google Scholar

• 60.

  Burleson-LesserKMoroneFTomassoneMSMakseHA. K-core Robustness in Ecological and Financial Networks. Sci Rep (2020) 10. 10.1038/s41598-020-59959-4

  • CrossRef
  • Google Scholar

• 61.

  wwwsecuringdemocracygmfusorg. Russia’s Affront on the News: How Newsfront’s Circumvention of Social media Bans Demonstrates the Need for Vigilance (2021). Available from: securingdemocracy.gmfus.org (Accessed February 20, 2022).

  • Google Scholar

• 62.

  AguiarLClaussenJPeukertC. Catch Me if You Can: Effectiveness and Consequences of Online Copyright Enforcement. Inf Syst Res (2018) 29:656–78. 10.1287/isre.2018.0778

  • CrossRef
  • Google Scholar

• 63.

  ElbahrawyAAlessandrettiLRusnacLGoldsmithDTeytelboymABaronchelliA. Collective Dynamics of Dark Web Marketplaces. Sci Rep (2020) 10:18827. 10.1038/s41598-020-74416-y

  • CrossRef
  • Google Scholar

• 64.

  NarayananVBarashVKellyJKollanyiBNeudertLMHowardP. Polarization, Partisanship and Junk News Consumption over Social media in the Us (2018).

  • Google Scholar

• 65.

  ChenWPachecoDYangKCMenczerF. Neutral Bots Probe Political Bias on Social media. Nat Commun (2020) 12:5580.

  • Google Scholar

• 66.

  van der LindenSPanagopoulosCRoozenbeekJ. You Are Fake News: Political Bias in Perceptions of Fake News. Media, Cult Soc (2020) 42(3):460–70. 10.1177/0163443720906992

  • CrossRef
  • Google Scholar

• 67.

  GarrettRKBondRM. Conservatives' Susceptibility to Political Misperceptions. Sci Adv (2021) 7:eabf1234. 10.1126/sciadv.abf1234

  • CrossRef
  • Google Scholar

• 68.

  BaptistaJPGradimA. Understanding Fake News Consumption: A Review. Soc Sci (2020) 9:185. 10.3390/socsci9100185

  • CrossRef
  • Google Scholar