Misinformation is a persistent problem that threatens societies at multiple levels. In spite of the intensi ed attention given to this problem by scientists, governments, and media, the lack of awareness of how someone has interacted with, or is being exposed to, misinformation remains to be a challenge. This paper describes MisinfoMe; an application that collects ClaimReview annotations and source-level validations from numerous sources, and provides an assessment of a given Twitter account, and the followed accounts, with regards to how much they interact with reliable or unreliable information.
Misinformation is an old problem, ampli ed in recent years by social media.
Much research tackled this problem from social as well as technical perspectives,
to better understand the phenomenon, how it spreads online, how it can be
automatically detected, its impact on opinions, etc. Nevertheless, research shows
that making people aware of how they interact with misinformation remains to be
a challenge [
This paper introduces MisinfoMe;1 an application that uses ClaimReview semantic annotations and other information validators to highlight the tweets that point to reliable or unreliable sources and content. MisinfoMe also expands the assessment to the Twitter users followed by the given one, to re ect the amount of misinformation they pass on to the given user. More speci cally, the objectives of the MisinfoMe tool are: 1. Create a (mis)information collection of 68K URLs and 1.3K domains, generated by 86 fact-checking organisations, 2. Collect the Twitter timeline of a given user and the accounts they follow, ? Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 1 http://socsem.kmi.open.ac.uk/misinfo/ 3. Highlight tweets with URLs pointing to false/true/mixed information, 4. Compare the given user with the \average user" with respect to the level of interaction with reliable and unreliable information, 5. Highlight the levels of misinformation interaction by followed twitter accounts and rank them accordingly.
In the following sections we brie y describe related work, the dataset used by MisinfoMe, and the main components of the application.
Demo: MisinfoMe will be demoed at the conference. Users can enter a Twitter handle to see the assessment of their levels of misinformation interaction, and the tweets that belong to each category (misinforming, valid, mixed). A video recording of the demo is available at: https://tinyurl.com/yxeun2nt and the full application is accessible at: http://socsem.kmi.open.ac.uk/misinfo/ 2
Several applications have been developed to help validating di erent types of content (images, bot accounts, news sources, reviews, etc.). NewsGuard (https: //newsguardtech.com) is a browser plugin that uses journalists to assess the reliability of websites (e.g., BBC, Wikipedia, Breitbart) following generic credibility and transparency indicators. B.S.Detector (tinyurl.com/y368cbhw) is another browser plugin that o ers validations of news sources. ClaimBuster (idir.uta.edu/claimbuster/) detects and ranks claims, and matches them to fact-checks. Botometer (botometer.iuni.iu.edu) calculates the likelihood of a Twitter account being a bot based on various activity patterns. TinEye (tineye.com) provides a reverse image search to help track out of context image usage. Rbutr (rbutr.com) connects web pages that present disputes or contradictions, provided by the user community, and provide them to users when browsing such pages. Fakespot (fakespot.com) assesses the validity of online reviews on sites such as Amazon and Tripadvisor.
Unlike the tools above, MisinfoMe is focused on revealing how a given Twitter account, and the accounts it follows, have been interacting with misinformation. MisinfoMe does not o er new content validations per se, but rather it uses thirdparty fact-checking and source validations to assess all the URL-bearing Tweets in the timeline of the accounts in question.
Below we brie y describe the main components of MisinfoMe and its user interface. An in-depth description can be found on the MisinfoMe site itself. 3.1
MisinfoMe collects ClaimReview2 annotations (see example below) from different fact-check publishers and some aggregators.3 ClaimReview is a standard schema used for fact-checking reviews of claims, and from it we collect
MisinfoMe: Who is Interacting with Misinformation?
the URLs (if present) of the fact-checked article, and the validation outcome
(rating). We then add other publicly available source-assessments, e.g., from
OpenSources,4 Wikipedia,5 and BuzzFeedNews,6 combined with the approach
described in [
"url" : "https://www.facebook.com/TND/videos/358402408404240/" } }, "reviewRating": { "alternateName": "Mostly False", "ratingValue": "3", }, "url": "https://www.politifact.com/truth-o-meter/statements/2019/aug/27/
joe-biden/biden-wrong-when-he-says-trump-hasnt-condemned-dav/" } 3.2
Given a Twitter handle, MisinfoMe uses the Twitter API to collect its timeline. URLs appearing in the tweets are then matched with the dataset described above. MisinfoMe displays how many tweets were collected, how many contain a URL (Figure 1), and how many of these URLs point to reliable, unreliable, or mixed pages and sources. To unshorten the URLs, we perform a HTTP HEAD request and follow the HTTP redirections. We also extract the domain name of each URL to compare with domain-level validations.
As shown in Figure 2, users can scroll the list of tweets that were assessed, see evidence to support their placement into the reliable, unreliable, or mix category, and the source of this evidence (e.g., Snopes.com). A bar-chart comparison of the given Twitter account with the average of over 770 randomly selected account, is also shown on MisinfoMe (not shown in gures). 3.3
In addition to assessing the given Twitter account with regards to sharing misinformation, MisinfoMe also assesses the accounts followed by the given one with
regards to their spread of misinformation, and ranks them accordingly. Figure 3 shows a selection of the accounts followed by the given account, colour-coded to re ect their tendency to sharing reliable (green), unreliable (red), or mixed (orange) information. Grey indicate that none of their shared URLs were found in our dataset.
This paper introduced MisinfoMe; an application for assessing the sharing of unreliable information posted by a Twitter account and the accounts it follows. We described the data collection and the analysis performed, and the main user interface components. We are currently planning several user-based experiments, to evaluate the interface, the datasets, and to assess the impact of MisinfoMe on people's awareness of their interactions with misinformation, or of those they are interested in (e.g., celebrities, politicians).
Acknowledgment Co-Infom, H2020, grant agreement 770302.