Personal genomics offers a complex form of uncertainty in which a person’s data are largely stable, but the interpretation and implications continue to evolve with the emergence of new research. Another domain, in which there is uncertainty about the supporting evidence and truthfulness of a claim, is social networks. We propose that a similar method can be used to communicate uncertainty in these contexts, and present a tool for visualizing social network claims that builds upon research in both contexts

Ball, Madeleine

Finn, Samantha T

Metaxas, P. Takis

Nov, Oded

Pollalis, Christina

Shaer, Orit

Verish, Clarissa

Westendorf, Lauren

English

Wellesley College

Wellesley College
Wellesley College Digital Scholarship and Archive
Faculty Research and Scholarship
5-7-2017
From Personal Genomics to Twitter: Visualizing
the Uncertainty of Evidence
Lauren Westendorf
lwestend@wellesley.edu
Christina Pollalis
cpollali@wellesley.edu
Clarissa Verish
cverish@wellesley.edu
Orit Shaer
oshaer@wellesley.edu
P. Takis Metaxas
pmetaxas@wellesley.edu
See next page for additional authors
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Recommended Citation
Lauren Westendorf, Christina Pollalis, Clarissa Verish, Orit Shaer, Panagiotis Takis Metaxas, Samantha Finn, Madeleine Ball, Oded
Nov. "From Personal Genomics to Twitter: Visualizing the Uncertainty of Evidence". Proceedings of the Designing for Uncertainty in
HCI: When Does Uncertainty Help? Workshop on CHI, 2017.
Authors
Lauren Westendorf, Christina Pollalis, Clarissa Verish, Orit Shaer, P. Takis Metaxas, Samantha T. Finn,
Madeleine Ball, and Oded Nov
This conference proceeding is available at Wellesley College Digital Scholarship and Archive: http://repository.wellesley.edu/
scholarship/146
 From Personal Genomics to Twitter: 
Visualizing the Uncertainty of Evidence
 
 
Abstract 
Personal genomics offers a complex form of uncertainty 
in which a person’s data are largely stable, but the 
interpretation and implications continue to evolve with 
the emergence of new research. Another domain, in 
which there is uncertainty about the supporting 
evidence and truthfulness of a claim, is social networks. 
We propose that a similar method can be used to 
communicate uncertainty in these contexts, and 
present a tool for visualizing social network claims that 
builds upon research in both contexts. 
Author Keywords 
Uncertainty; personal genomics; personal informatics. 
ACM Classification Keywords 
H.5.m. Information interfaces and presentation (e.g., 
HCI): Miscellaneous. 
Background 
The personal genomic context offers a form of 
uncertainty not addressed by existing taxonomies and 
applications. Although personal genomic data are 
largely stable during a person's lifetime, the 
interpretation and implications of such data change 
over time as advances in medical research uncover 
relationships between genes and health. In traditional 
forms of personal informatics, uncertainty often arises 
from the accuracy and context of data tracking. In 
 
Lauren Westendorf 
Christina Pollalis 
Clarissa Verish 
Orit Shaer 
Wellesley HCI Lab 
Wellesley College 
Wellesley, MA, USA 
{lwestend, cpollali, cverish, 
oshaer}@wellesley.edu 
 
Panagiotis Takis Metaxas 
Wellesley College 
Wellesley, MA, USA 
pmetaxas@wellesley.edu 
 
 
 
 
 
Samantha Finn 
Wellesley College 
Wellesley, MA, USA 
sfinn@wellesley.edu 
 
Madeleine Ball 
Open Humans Foundation 
Brookline, MA, USA 
madeleine@openhumans.org 
 
Oded Nov 
New York University 
New York NY, USA 
onov@nyu.edu 
  
contrast, in genomic data 99.9999% accuracy is 
typical, and continues to improve [4]. However, the 
interpretation of the scientific evidence and its 
certainty, and the related implications for the user’s 
health, frequently evolve as new medical research 
reveals new relationships between a person’s genetic 
makeup and resulting effects. 
To address uncertainty in this context, in previous 
work, we developed GenomiX—an interactive visual tool 
to support self-exploration of personal genomic data 
[5], which helps users understand the interpretations’ 
level of certainty. GenomiX does not provide any new 
genetic interpretations, but instead provides new and 
easier ways for users to explore the data, drawing upon 
the publicly available interpretation provided by the 
Personal Genome Project (PGP) [3]. In GenomiX, gene 
variants are represented as bubbles plotted by potential 
health effect (low, medium, high) and the certainty of 
the scientific evidence (well-established, likely, 
uncertain) that links a gene variant with a trait or 
condition. The color and size of each bubble encode 
effect (pathogenic, benign, protective) and risk. These 
designations are drawn from the PGP database and are 
updated as new evidence is discovered. 
Encouraged by the positive acceptance of the tool by 
users who explored their own data [5], we seek to 
apply our approach to help non-expert users in 
domains beyond personal genomics to better 
understand the uncertainty associated with the 
interpretation and credibility of information. One 
domain, in which there is often uncertainty about the 
credibility of information, is social networks where the 
quality of supporting evidence and truthfulness of a 
claim is not clear. Users encounter stories shared by 
others in their network and are often unaware of the 
uncertainty regarding the trustworthiness of these 
stories. Following, we describe the application of 
GenomiX’s interactive visualization approach to 
communicating the uncertainty regarding the credibility 
of claims spread on Twitter using Twitter Trails [6].  
Twitter Trails  
TwitterTrails is an investigative and exploratory tool to 
analyze the origin and spread of a “story” on Twitter (a 
claim, a meme or an event). Prompted by a search with 
relevant keywords using the Twitter API, it collects and 
analyzes up to 200K tweets automatically. While it does 
not answer directly the question of a story’s validity, it 
provides information that a critically thinking person 
can use to examine how a Twitter audience reacts to 
the spreading of the story.  
 
Figure 1. TwitterTrails homepage. 
We used TwitterTrails to infer certainty and truthfulness 
of claims spread on Twitter using the following 
measures:  
  
Spread measures simultaneously the propagation and 
number of the highest reaching tweets [2]. It does not 
reflect the few tweets with the most retweets, nor does 
it measure how many tweets were collected. Although 
these numbers are interesting and meaningful, the 
spread is meant to give an overall picture of the impact 
of a story: how visible it was as well as how many 
people were engaged in it.  
Skepticism measures the prominence of doubt and 
mistrust in a story [2]. The first step to calculating the 
skepticism is to identify tweets in which the author 
expresses doubt in the validity of a claim, whether they 
are wondering if the claim is false or expressing that it 
is an outright lie. For now, TwitterTrails employs a 
simple algorithm, which works fairly well: identify 
tweets containing commonly used keywords to express 
doubt or disbelief (such as “hoax” or “fake”), which can 
be modified for individual stories and languages. Using 
this algorithm, data is separated into two subsets: 
those that express doubt and those that do not 
(implicitly expressing support). The skepticism of a 
story is defined as the ratio of the spread of doubting 
tweets to the spread of supporting tweets.  
Partisan bias is computed from characteristics of the 
co-retweeted network [1] that is formed using a force-
directed drawing algorithm using Gephi. This is done by 
measuring the size, density, and Euclidean distance 
between polarized groups of retweeters, while their 
political identity (liberal or conservative) is determined 
by the frequency of politically-charged keywords 
appearing in the group members' profile description.  
TwitterTrails’ current database of about 500 
investigated stories offers evidence that large enough 
crowds following a story are likely to react in ways that 
strongly correlates with the validity of a claim. True 
claims are likely to have relatively high spread with low 
skepticism, while false claims typically have low spread 
and high skepticism.  
Claims that have both low spread and skepticism are 
difficult to categorize, and we have found almost no 
examples of claims that have both high spread and 
high skepticism. 
Visualizing Twitter Claims 
Using data from TwitterTrails, we can plot claims and 
events on Twitter in a similar manner to the gene 
variants in GenomiX (Figure 2), where the x- and y-
axes represent the skepticism score and truth of the 
claim and the size of each bubble corresponds to the 
spread of the claim. The color encodes the partisan bias 
of the users discussing the claim, where claims 
discussed in a partisan echo chamber are more 
saturated and claims with more bipartisan discussion 
are gray or white. When a user selects a claim, detailed 
information about that claim (taken from the Overview 
in TwitterTrails) appears in the right sidebar, including 
a summary of the claim, the number of tweets and 
users discussing it, keywords, and the collection date.
  
 
Figure 2:  Visualizing TwitterTrails stories. Each bubble represents a twitter claim or event, plotted by truth of claim and skepticism. 
When a claim is selected, detailed information about that claim is displayed on the right.
Conclusion and Future Work 
Visualizing data in which reduce uncertainty is a 
challenge across many domains. In this paper, we used 
personal genomics and social media claims as unique 
and compelling contexts for communicating 
uncertainty. We also proposed extending visualization 
techniques from personal genomics to map the spread 
and skepticism of twitter claims using data from 
TwitterTrails. Our previous work in the domain of 
personal genomics aimed to encourage exploration and 
long-term engagement for non-expert users through 
the development and evaluation of a novel visualization 
tool. We extend this goal and hope to empower 
consumers of the news and social media users to better 
understand uncertainty associated with the credibility of 
stories. We also aim provide such users with visual 
tools which enable and encourage them to explore the 
propagation and supportive evidence of stories. Future 
work will include evaluation of the usability, usefulness 
and impact of our proposed tool. We believe that 
enabling users to map retweeted claims from their own 
account could lead to self-reflection about their role in 
misinformation propagation. 
Acknowledgements 
We thank our collaborators from Open Humans. This 
work is partially funded by NSF grant IIS-1422068. 
 References 
1. Samantha Finn, Panagiotis Takis Metaxas and Eni 
Mustafaraj. "Measuring Perceived Political 
Polarization through Collective Intelligence 
presented". Proceedings of the Conference on 
Political Networks, 2014. 
2. Samantha Finn, Panagiotis Takis Metaxas and Eni 
Mustafaraj.  2015. Spread and Skepticism: Metrics 
of Propagation on Twitter. in Proceedings of the 
ACM Web Science Conference, ACM, 39.  
3. Personal Genome Project. 2016. Retrieved January 
31, 2016 from http://www.personalgenomes.org/. 
4. Brock A Peters, Bahram G Kermani, Andrew B 
Sparks, Oleg Alferov, Peter Hong, Andrei Alexeev, 
Yuan Jiang, Fredrik Dahl, Y Tom Tang and Juergen 
Haas.  2012. Accurate whole-genome sequencing 
and haplotyping from 10 to 20 human cells. 
Nature, 487 (7406). 190-195.  
5. Orit Shaer, Oded Nov, Johanna Okerlund, Martina 
Balestra, Elizabeth Stowell, Lauren Westendorf, 
Christina Pollalis, Jasmine Davis, Liliana Westort, 
and Madeleine Ball (2016). “GenomiX: A Novel 
Interaction Tool for Self-Exploration of Personal 
Genomic Data.” Proceedings of the 2016 CHI 
Conference on Human Factors in Computing 
Systems, 661-672. 
6. Twitter Trails. 2016. Retrieved January 31, 2016 
from http://twittertrails.com//. 


From Personal Genomics to Twitter: Visualizing the Uncertainty of Evidence

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From Personal Genomics to Twitter: Visualizing the Uncertainty of Evidence

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