Science VS Conspiracy Information Dissemination Online

Digital misinformation has become so pervasive in online social media that it has been listed by the WEF as one of the main threats to human society. Whether a news item, either substantiated or not, is accepted as true by a user may be strongly affected by social norms or by how much it coheres with the user’s system of beliefs (32, 33). Many mechanisms cause false information to gain acceptance, which in turn generate false beliefs that, once adopted by an individual, are highly resistant to correction (34⇓⇓–37). In this work, using extensive quantitative analysis and data-driven modeling, we provide important insights toward the understanding of the mechanism behind rumor spreading. Our findings show that users mostly tend to select and share content related to a specific narrative and to ignore the rest. In particular, we show that social homogeneity is the primary driver of content diffusion, and one frequent result is the formation of homogeneous, polarized clusters. Most of the times the information is taken by a friend having the same profile (polarization)––i.e., belonging to the same echo chamber.

We also find that although consumers of science news and conspiracy theories show similar consumption patterns with respect to content, their cascades differ.

Our analysis shows that for science and conspiracy news a cascade’s lifetime has a probability peak in the first 2 h, followed by a rapid decrease. Although the consumption patterns are similar, cascade lifetime as a function of the size differs greatly.

These results suggest that news assimilation differs according to the categories. Science news is usually assimilated, i.e., it reaches a higher level of diffusion, quickly, and a longer lifetime does not correspond to a higher level of interest. Conversely, conspiracy rumors are assimilated more slowly and show a positive relation between lifetime and size.

The PDF of the mean-edge homogeneity indicates that homogeneity is present in the linking step of sharing cascades. The distributions of the number of total sharing paths and homogeneous sharing paths are similar in both content categories.

Viral patterns related to distinct contents are different but homogeneity drives content diffusion. To mimic these dynamics, we introduce a simple data-driven percolation model of signed networks, i.e., networks composed of signed edges accounting for nodes preferences toward specific contents. Our model reproduces the observed dynamics with high accuracy.

Users tend to aggregate in communities of interest, which causes reinforcement and fosters confirmation bias, segregation, and polarization. This comes at the expense of the quality of the information and leads to proliferation of biased narratives fomented by unsubstantiated rumors, mistrust, and paranoia.

According to these settings algorithmic solutions do not seem to be the best options in breaking such a symmetry. Next envisioned steps of our research are to study efficient communication strategies accounting for social and cognitive determinants behind massive digital misinformation.


Folksonomies: social networks misinformation

/society (0.427331)
/technology and computing/enterprise technology/customer relationship management (0.382669)
/style and fashion/accessories/sunglasses (0.205743)

Conspiracy Information Dissemination (0.970384 (negative:-0.580066)), Online Digital misinformation (0.945878 (negative:-0.580066)), online social media (0.929804 (negative:-0.580066)), content diffusion (0.911061 (neutral:0.000000)), consumption patterns (0.899705 (positive:0.384477)), similar consumption patterns (0.893218 (positive:0.384477)), homogeneous sharing paths (0.893047 (neutral:0.000000)), extensive quantitative analysis (0.892763 (positive:0.388343)), simple data-driven percolation (0.891339 (positive:0.341614)), science news (0.883636 (positive:0.384477)), massive digital misinformation (0.879483 (neutral:0.000000)), total sharing paths (0.872222 (neutral:0.000000)), social homogeneity (0.869861 (neutral:0.000000)), signed edges accounting (0.866283 (neutral:0.000000)), settings algorithmic solutions (0.861210 (positive:0.521072)), efficient communication strategies (0.857629 (neutral:0.000000)), fosters confirmation bias (0.856979 (negative:-0.387842)), higher level (0.853837 (positive:0.200425)), mean-edge homogeneity (0.843617 (neutral:0.000000)), homogeneity drives (0.833440 (neutral:0.000000)), conspiracy news (0.801241 (neutral:0.000000)), cascade lifetime (0.790284 (neutral:0.000000)), social norms (0.787112 (positive:0.301778)), conspiracy theories (0.785635 (positive:0.384477)), data-driven modeling (0.783358 (positive:0.388343)), news item (0.783078 (neutral:0.000000)), false information (0.782036 (neutral:0.000000)), longer lifetime (0.778907 (negative:-0.212053)), human society (0.775385 (negative:-0.580066)), false beliefs (0.773534 (negative:-0.402220))

social media:FieldTerminology (0.808076 (negative:-0.580066)), WEF:Organization (0.773706 (negative:-0.580066))

Sociology (0.926256): dbpedia | freebase | opencyc
Conspiracy theory (0.707951): dbpedia | freebase
Homogeneity (0.612860): dbpedia
Cascade Range (0.610653): dbpedia | freebase
World Economic Forum (0.609605): website | dbpedia | freebase | yago
Social media (0.595321): dbpedia | freebase
Cascade (0.594093): dbpedia
Assimilation (0.582345): dbpedia
Scientific method (0.572030): dbpedia | freebase
Homoscedasticity (0.568700): dbpedia | freebase
Heterogeneity (0.557660): dbpedia
Assimilation (0.554745): freebase

 The spreading of misinformation online
Periodicals>Journal Article:  Del Vicarioa, Bessib, Zolloa, Petronic, Scalaa, Caldarellia, Stanleye, Quattrociocchia (December 4, 2015 ), The spreading of misinformation online, Retrieved on 2017-01-23
  • Source Material []
  • Folksonomies: social networks misinformation