GalaxyScope – Finding the “Truth of Tribes” onSocial Media

Joao Marcos Da Oliveira1, Peter A. Gloor2
1galaxyadvisors AG, Aarau, Switzerland 2 MIT Center for Collective Intelligence,Cambridge, USA. jmarcos@galaxyadvisors.com, pgloor@mit.edu

Abstract This paper introduces GalaxyScope, a novel system to distinguishdifferent interpretations of “truth” for different virtual tribes. It extracts the tribes
from Wikipedia through analyzing its categories “Ideologies”, “Lifestyles”, and“Culture”, leading to tribes such as “capitalism”, “socialism”, and “liberalism”. It
then calculates the most influential “tribe leaders” through their association on
Wikipedia with these concepts. To score their influence in Wikipedia, we use anovel metric we call “reach2” which measures how many people somebody canreach within two degrees of separation on Wikipedia living people pages. It
subsequently calculates the vocabulary on Twitter of the tribe leaders, and uses
these words to automatically assign individuals to tribes, as well as calculating therelevance of text documents such as tweets or news items for each tribe.

1 Introduction
Quantum physics suggests that there are many different universes, with ourcurrent world being embedded into just one out of infinitely many other universes.
Currently it seems humans are breaking up locally in many different multiverses.
Each of these universes has its own reality, defining fact or fiction for theinhabitants of the universe. Each universe is inhabited by its own tribe, with itsown belief system. Based on most recent history, we can for instance group thesedifferent multiverses into three main universes, each inhabited by a different tribe.
The three tribes are the ultra-patriots, which we term the “fatherlanders”, thetechnocrats, which we call the “nerds”, and the environmentalist, here called the“treehuggers”. We use these three simple tribes to explain the basic idea, later inthe paper we will describe how we develop a more fine-grained tribal hierarchy.
While members of these three tribes are spread out around the world, living side
by side, they are living in different worlds. Members of these three tribes live inall countries, although usually members of one tribe dominate a particular region
or country. The fatherlander tribe wants to recreate the national states of the earlytwentieth century with strong borders protected by fences and walls. The members

2of the nerd tribe are believers in a global world ruled by capital and technology.
Together with the members of the third tribe, the treehuggers, who want toconserve nature and the environment, they are currently on the defense against thefatherlanders.
Each of the three virtual tribes is living in their own reality, and is defining theirown truth. Law and order, family values, and God and fatherland are theundisputed foundation of the fatherlanders, as is unquestioning belief in progress
through science and technology for the progressives, and the quest to conserve theenvironment while restricting economic growth at any price for the treehuggers.
Note that while we have split humanity in three main tribes for this introductory
analysis, reality is much more complex, and all of us are members of manysmaller and larger tribes. In the remained of this paper we describe a system toautomatically categorize anybody into their different tribal affiliations, and also
check for the “truth content” of particular statements in the context of each tribe.

2 Building a Tribe Detection System
Through Coolhunting in social media, on newsfeeds, Twitter, Wikipedia, andonline forums we can try to shed some light on these alternate realities. Whenlooking at news, key topics, and social networks among the key people in each ofthese universes, we can make these different echo chambers transparent, and try toincrease awareness among the different tribes for alternate realities. What is fakenews for one universe is fact for another!
With the cacophony of new information appearing daily on social media and theWeb, a tool to separate fact from fiction is sorely needed [3]. The continuous
stream of tweets from Donald Trump and other influencers in the alt-right spherecreates a confusing mix of real and fake news [5]. The goal of the proposedsystem GalaxyScope, is to categorize and visualize News information on social
media and the Internet. GalaxyScope will extract the most relevant informationabout● key people● key brands and topics● fake and real newson any search topic, such as for example “Steve Bannon”, “Pizzagate” or “Nike”.
GalaxyScope is motivated by the three “T”’s: “Trust”, “Transparency”, and“Tribes”. Through making the influencers behind fake and real news transparent,
it will increase trust by identifying the tribes, whose members are living in theirown separate alternate realities.

32.1 Analyzing Fake News
What is truth for one tribe, is fake news for the other tribes [4]. For example, at theend of the 2016 US Presidential elections, in early November an absurd claim
was made, accusing Hillary Clinton to run a pedophile ring out of a pizza
restaurant in Washington. Called “pizzagate”, it became a favorite call to arms
among right-wing extremists and Donald Trump supporters, leading one incensed
fanatic to drive a few hundred miles from Salisbury, North Carolina toWashington DC, and firing his automatic gun in the pizza restaurant. most of thisrumor spreading was happening on social media.
The next picture shows the word cloud generated from the 18,000 tweets, most of
the words are dark red, indicating that they are used in negative context. The
word “Clinton” is in dark red, as the tweeters are mostly accusing Hillary Clinton
to molest little children. The word “Trump” stands out in green, as they see him as
the savior.
Fig. 1. Twitter Word Cloud generated from 18,000 tweets about “pizzagate”.(words in red havenegative sentiment).

Figure 2 below shows the Twitter network, each node is a person tweeting, alink between two people means either that one person is retweeting a tweet sent by
the other person, or is mentioning the other person in a tweet.
There is a large cluster in the center of the network, made up of believers in thefake news. They are reinforcing each other, and increasing the traffic in their echochamber. The few supporters of Hillary, trying to debunk the fake news, arepushed aside, their tweets are ignored by the large echo chamber of conspiracy
theory believers. The people in the periphery (the “asteroid belt”) are tweeting into
the void, as their tweets are ignored from friends and foes alike.

4Fig. 2. Twitter retweet network generated from 18,000 tweets about “pizzagate”.

Figure 2 illustrates the challenges that an automatic fake news detection system
faces. While the fatherlanders discussions pizzagate form a large connectedcomponent on Twitter, the nerds defending Hillary Clinton, and debunking the
fake news, are quite isolated, and are not spreading each other’s news. In order toidentify the pizzagate discussion leaders (which we call the tribe’s leaders), we
need to calculate their network position using metrics such as betweenness
centrality. At the same time, as the Word Cloud in figure 1 illustrates, theconsistent word usage and sentiment will also identify the context of the fakenews, with the fatherlander’s tribe’s leader (Trump) shown in positive context,
and the rest of the words shown in consistently negative context. The discussion
about the same news item by another tribe, the nerds, would show the samedescriptive words, with the same sentiment, but with the other tribe’s leader(Trump) in negative context.
Since the 2016 US Presidential Elections fake news have achieved new
prominence, with right- and left-wing journalists accusing each other to spreadthem. Fact checking news Web sites have been set up, where paid staff andvolunteers manually check the accuracy of news (Alcott & Gentzkow 2017). Inearlier work, researchers have identified fake tweets by cross-checking them with5

trusted news sources such as Reuters or Bloomberg (Gupta et al. 2013, Gupta etal. 2013b, Gupta 2014). Additionally, Jimmy Wales, the founder of Wikipedia,
has also created a community-based initiative to fact-check fake news.
In this paper, we take the declaration of news by different tribes as “fake” or notas one of the main motivations for our tribe detection and classification system.

3. Our Solution
A tribe can be described as a concept, idea or a thing that its members believe
or like. To define the tribes we created a system named "TribeCreator", thissystem automatically help the user to find people that belong to a defined tribe.
The TribeCreator does this by using four different search functions: Profile
description, tweets, followers, and friends.
The "profile description" can be used to find a user that has a description thatmatches with the tribe (for example "I love nature" is a description that matcheswith the concept of a Treehugger). Search for tweets can be useful to identify
people that are tweeting about a certain topic or hashtag, like
#ArtificialIntelligence or #blockchain for the "nerd" tribe. The "followers" and"friends" functions can be used to find users that follow or are followed by aTwitter account, using this function we can find users that are connected with anaccount that post tweets related to the tribe, showing an interest in the subject.
Fig. 3. Example of a profile search for “I love nature”.

6Once we have identified members of each tribe we can collect their Twittertimeline, those tweets allow us to create a tribal vocabulary and our machinelearning model to find the tribes of a given user. We now also have the data toanalyze what those tribe members are posting about. To understand the context ofa trive, we created three visualizations: First, a network view that allows us to see
the top 50 nodes by reach2 for the tribe. Second, a hashtag word cloud view toidentify the top hashtags for the tribe, and finally a table with the most frequent
links posted by it members.
Fig. 4. Network view illustrating that different actors have a different influence in each tribe, forexample the size of Donald Trump in the fatherlander tribe is bigger than his size on the nerdtribe

7Fig. 5. Twitter hashtags for Fatherlander and Nerd tribe in TribeCreator

To identify the most influential leaders overall, we are using Wikipedia. One of
the most popular categories in Wikipedia are the people pages. Wikipedia editors
have put together “notability criteria” that clearly define if a person deserves
inclusion into Wikipedia or not. We use the people pages in Wikipedia to identify
the most influential people as well as the tribes they belong to through their links
to the most central concept pages such as “veganism”, “liberalism”, “socialism”,etc.. At the same time we construct co-occurrence links for the most significant
pages for a person found by a subject-driven centrality-measuring algorithm
similar to Google’s page rank. While Google’s page rank is fixed for eachwebsite, giving them a determined value, our ranking algorithm is topic-related.
E.g. the “Huffington Post” has very high relevance for debates about politics, buta low relevance for conversations about “pet food” (Gloor et al. 2016).
Table 1 illustrates the most influential tribal leaders, based on Wikipedia’speople pages. We measure the importance of people through calculating theirreach-2 metric on Wikipedia, i.e. the number of other Wikipedia people pages
than can be reached within two degrees of separation from a particular people
page. Reach-2 is a proxy for social capital, as it basically measures the influence8of the people a person is connected to.
Table 1 Top most influential living people in the English Wikipedia, sorted by reach-2

Namebarack obama
george w bush
donald trump
bill clinton
hillary clinton
bob dylanelton johnbarbra streisandsteven spielberg

reach2111398
89476
73081
68839
66538
51433
49950
49514
48903

As table 1 illustrates, the most influential people are the most recent US
presidents plus Hillary Clinton, followed by two musicians and two celebrities
from the film industry. Figure 6 shows the distribution of the different professions
among the top 100 most influential people by reach-2. Show business celebrities
dominate the list, as 73 out of the top 100 are actors and musicians.

Fig. 6 Percentage of domains of top 100 most influential people in English Wikipedia

The first spiritual leaders, Pope Francis (position 107), and the Dalai Lama(270) come much further down in the ranking. The same is true for businessleaders, with Bill Gates being the most highly ranked leader coming at position132. Philosophers and other thinkers of big ideas are even further down the list.

Figure 7 illustrates the Wikipedia link network collected with the category
fetcher of Condor (Gloor 2017), which constructs a link network for all pages

9belonging to a particular category. The network in figure 3 includes all pages
belonging to the category “Ideologies”. The size of a node is its betweenness inthe network. As we can see, the most influential concepts, and thus a candidate fora tribe are “Socialism”, “Capitalism”, “Liberalism”, and “Feminism”. The sameprocess has been repeated for the Wikipedia categories “Lifestyles” and“Cultures” (renamed “recreation” for clarity in figure 6 below).
Fig. 7 Four tribal categorizations derived from Wikipedia categories

Alternate
Reali+esIdeologies

Lifestyles

Recrea+on

Our final approach combines the pieces described above, using dynamic
semantic social network analysis, NLP (natural language processing) and machinelearning. Figure 8 illustrates the main component of our system. We calculate abipartite graph of all Wikipedia concepts, linking them by the people sharing thesame concept. For example, Bill Clinton is connected to the three concepts“vegan”, “environmentalism” and “US president”. By ranking the most importantconcepts we identify the tribes.

10Fig. 8 Key Components of GalaxyScope

In the last step, we assign an individual person through their word usage on theFacebook wall, or the Twitter feed to a particular tribe based on similar word
usage to the tribal vocabulary. To match a certain brain, or a certain news item to atribe, we can do a similar word matching to the bag of words calculated for thetribe.
Figre 9 below illustrates the tribefinder component of GalaxyScope, Entering
the Twitter id of a person (US Vice President Pence, and Elon Musk in theexamples below) shows their tribal affiliations, Pence is a big fatherlander, while
Musk comes out as a nerd. Similarly we can see their lifestyles (both are membersof the “YOLO (“You Only Life Once”_ tribe, while Musk also has a largetranshumanist affiliation.

11Fig. 9 Tribefinder Components of GalaxyScope124. Conclusion
We have introduced a new way of measuring “truth” for different segments ofsociety. We identify the most influential tribes, through their tribal leaders on
Wikpedia, as well as their vocabulary on Twitter. This allows us to assign otherpeople, brands, ideas, and news items to tribes. While first results are encouraging,much more work is needed to make this a useful and scalable system.

References
1. Gloor, P. (2017) Sociometrics and Human Relationships: Analyzing Social
Networks to Manage Brands, Predict Trends, and Improve Organizational
Performance, Emerald Publishing, London

2. Gloor, P. Marcos, J. de Boer, P. Fuehres, H. Lo, W. Nemoto, K. (2016) CulturalAnthropology through the Lens of Wikipedia in: X. Fu, J.-D. Luo, M. Boos(eds.), Social Network Analysis: Interdisciplinary Approaches and Case,
Chapter 10, CRC Press, Taylor & Francis Group, ISBN 1498736645, October
3. Yasseri, T., Spoerri, A., Graham, M., & Kertész, J. (2013). The mostcontroversial topics in Wikipedia: A multilingual and geographical analysis.
4. Petrovic, S., Osborne, M., McCreadie, R., Macdonald, C., Ounis, I., &Shrimpton, L. (2013, June). Can Twitter replace newswire for breaking news?.
In Seventh international AAAI conference on weblogs and social media.
5. Becker, H., Naaman, M., & Gravano, L. (2011). Beyond Trending Topics:
Real-World Event Identification on Twitter. ICWSM, 11(2011), 438-441.
6. Allcott, H.; Gentzkow, M. (2017). Social Media and Fake News in the 2016
Election. http://www.nber.org/papers/w23089
7. Gupta, A.; Lamba, H.; Kumaraguru, P.; Joshi, A. (2013). Faking Sandy:
Characterizing and identifying fake images on Twitter during Hurricane Sandy.
In Proceedings of the 22nd International Conference on World Wide Web(WWW '13 Companion). ACM, New York, NY, USA, 729-736.
8. Gupta, A.; Lamba, H.; Kumaraguru, P. (2013). "$1.00 per RT#BostonMarathon #PrayForBoston: Analyzing fake content on Twitter," 2013
APWG eCrime Researchers Summit, San Francisco, CA, 2013, pp. 1-12.
9. Gupta A.; Kumaraguru P.; Castillo C.; Meier P. (2014). TweetCred: Real-Time
Credibility Assessment of Content on Twitter. In: Aiello L.M., McFarland D.

13(eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science,
vol 8851. Springer, Cham