Black Lives Matter protests have been dominating my Twitter feed for the past several days. Google Trends data shows a similar trend:
But is it the same experience across the US?
It turns out that interest is strongly related to political affiliation. Here is a scatter plot of Google Trends interest in “Black Lives Matter” at the US state level, compared to the percentage of the vote for Clinton in the 2016 presidential elections.
The fit is pretty remarkable (55%), with states that have more Democrat votes showing more interest in the topic.
State which defy the trend are Utah, Idaho, and Wyoming (more interest in the topic than expected by their voting patterns) and, on the opposing side, Mississippi, South Dakota, and Florida (less interest than expected). Also, anecdotally, the “Related queries” shown by Google Trends in California and Oregon are related to donations to Black Lives Matter, whereas in Mississippi and Florida they are for merchandise.
I also tested interest in the terms “protests” and “looting” across the different states. The former behaves similarly to “Black Lives Matter” while the latter had a similar trend to that of “Black Lives Matter”, only breaking for highly Democrat states, where there was far less interest in looting than expected by the overall trend.
Political scientists may want to theorize if this will change election results, but at least overall the pattern seems to suggest that this is (still?) an issue where interest depends on who you vote for.
Addendum (16 June 2020):
In July 2016 large-scale Black Lives Matter demonstrations were held in 88 cities. Google Trends data from that period (May – July 2016) shows no correlation (R2=0.00) with voting patterns.
I attended the first virtual edition of TheWebConference last week. The conference was planned as a conference with physical attendance. The organizers decided to make it a virtual conference a few weeks before it began. I have to applaud the organizers who managed to make this change which is extremely challenging on many levels.
I attended several sessions and I have to say that my experience was not wholly positive (by no fault of the organizers), partly due to objective reasons and partly because of things that we might learn to do differently.
Objective problems: A virtual conference offers the possibility for many more people to attend. On the other hand, it also means that there are significant time zone problems. Owing to the location of Taiwan, I’m guessing that people from time zones of India and up to Australia could probably comfortably attend the entire conference. People in the west coast of the Americas likely attended the morning sessions and those in Europe the afternoon sessions. I’m not sure what people on the east coast of the Americas did… I don’t see how we can overcome this problem, but time zone differences mean that the conference audience is spread over multiple sessions. All the sessions I attended had fewer participants than what I would have expected in a physical conference.
Things we can do differently:
Questions and participation: For some reason, it felt as though people were less comfortable asking questions. This was true even at a virtual poster session which I participated in. Perhaps, just as we have a Session Chair, we should have a secret “session question asker”, who will ask the first questions to help others participate? (and no, the fact that the Chair asks a question didn’t prove to be a good solution)
Socializing: One of the main reasons I go to a conference is to have informal conversations with people. This didn’t happen in the virtual setting, and I don’t know how we can make it work. Perhaps hold virtual lunches?
Disconnect from other work: One advantage of going to a conference somewhere is that I (mostly) disconnect from other work and dedicate those few days to being immersed in the conference. Since I was home, it was much harder to disconnect. I felt as though the conference was the side show to my usual work. This is probably something I can learn to do…
Virtual conferences potentially have advantages over physical conferences, for example, in the fact that they open their (virtual) doors to more people, some of whom would not be able to travel to a physical conference. As more conferences will be moving to a virtual setting, at least for the next few months, we should give more thought to how we maintain the benefits of the physical conference as well as realize the benefits of being on a virtual conference.
Internet advertising systems know a lot about us. If you
want to know how much, head to Google’s web page on ad personalization (https://adssettings.google.com/u/0/authenticated).
On a recent visit I found out that Google knows of my upcoming travel plans to
Italy and Texas, a few of my hobbies, and several academic topics that I’m
learning more about. Unsurprisingly, it wasn’t correct on everything (I’m not
into extreme sports), but it knew a lot more than I would have imagined before
I visited their web page.
Over the past few years our and other research groups have shown that interactions with search
engines can be used to screen people for a variety of serious medical
conditions, both mental and physical. These include depression, eating
disorders, Parkinson’s disease, several types of solid tumor cancer, and
diabetes. However, informing people about these inferences is challenging both
technically and ethically.
This week we published a paper (https://dl.acm.org/doi/10.1145/3373720)
showing how to leverage the information that Internet advertisers have about us,
to screen for 3 types of cancer. Our results suggest that it’s indeed possible
to screen people for their likelihood of suffering from cancer before they are
diagnosed by a doctor.
Here’s how it works: when an advertiser uses Bing or Google
to advertise, they select keywords such that when a user searches for these
keywords their ads are shown. A
more sophisticated form
of advertising happens when, in addition, advertisers tell Google or Bing whenever a user who saw the ads buys the product they
were trying to sell. When advertisers do this, the advertising systems learn to
predict who, among all people use the keywords, are likely to buy a product (technically
this is known as conversion optimization). This learning is based on the
information that advertising systems have about users, including their
interests, locations and demographics.
What we did was to leverage this capability and use the
advertising system to screen people for cancer. We achieved this by showing an ad to people who searched
for information on self-diagnosis of lung, breast or colon cancers. The ad suggested help in understanding the
severity of the symptoms that people were experiencing. People who chose to click
the ads were directed to a website where, after explaining the experimental
nature of the system and asking for their consent, they were given a clinical
questionnaire about their demographics and symptoms. People who answered the questions were
given one of two indications: either that they should urgently seek medical
attention, because their symptoms were deemed serious, or that it was likely
that their symptoms weren’t indicative of cancer but medical advice should be
sought, though not urgently.
When the questionnaire indicated that a person was likely
suffering from cancer, we informed the advertising system that the person “bought”
our “product”. Within 3 weeks, the advertising systems learned to focus on
those people who probably have cancer, such that approximately 1 in 10 people who
completed the questionnaires were likely suffering from it, up from the
baseline rate of under 1%. This rate was similar for all three types of cancer.
The use of ads offers a method for interacting with people
who might be suffering from as-of-yet
undiagnosed cancer. By providing ads with an offer of help and
empowering people to select whether or not they wished to receive this help, we
overcome many of the ethical challenges associated with unsolicited diagnosis.
Our use of the sophisticated capabilities and knowledge about users that
advertising systems have, allows us to identify people with serious disease,
without having to have access to sensitive individual-level search data.
Interestingly the people who use the system most came from
countries with high Internet use and lower life span. The latter is a known proxy for the
quality of the health system.
Many health organizations use internet advertising for awareness
campaigns and for campaigns designed to encourage healthier behaviors. Our results lead us to
suggest that health systems should leverage the information that advertising
systems collect about people in order to improve population level screening
Jeffrey Hammerbacher (at the time at Facebook) once commented
that “The best minds of my generation are thinking about how to make people
click ads. That sucks.” Let’s make use of the products of those great minds to
improve outcomes for people with serious disease.
One of the most common ways to model the spread of an infectious disease in a population is through compartment models, so called because they divide the population into compartments, with each person residing in one compartment. Perhaps the most common variant is the Susceptible-Infected-Recovered (SIR) model, where people are in one of those 3 compartments. A simple set of 3 differential equations describes the movement of people between these compartments. Thus, for example, the number of infected people in the next time step is dependent on the number of currently susceptible individuals, the number of infected people they come into contact with, and the infection rate, minus the number of people who recover in a time step.
most cases people don’t just belong to one compartment, because populations are
not homogenous. For example, it makes sense to divide the population not just
to the SIR compartments but also according to the country they live in.
publish an extended SIR model which can model homogeneous populations, divided,
for example, by area of residence, age group, etc. By fitting the model to Google
Trends data for two common viruses, we reveal information about the
complex spatial structure of disease spread.
The viruses we rested were Respiratory Syncytial Virus (RSV) and West Nile Virus (WNV). No COVID-19 data here. Sorry.
make no prior assumptions on spatial structure, human movement patterns
in the US explain 27%–30% of the estimated inter-state transmission rates. The
transmission rates within states are correlated with known demographic
indicators, such as population density and average age.
Our model also allows prediction of disease spread in subsequent seasons using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season.
The work was done mostly by our then intern, Dr. Inbar Seroussi.
On September 6th this year the Centers for Disease Control and Prevention (CDC) put out an Investigation Notice concerning a (suspected) outbreak of lung illness associated with using E-cigarette products. According to this notice, CDC is reviewing reports of a severe pulmonary disease associated with E-cigarette products, Following reports from 33 US states.
People who are suspected to have this disease report the following symptoms:
cough, shortness of breath, or chest pain
nausea, vomiting, or diarrhea
fatigue, fever, or weight loss
Brief summary if you want to decide whether or not to read further: Bing data seems to show that these symptoms appear in people who are likely using E-cigarettes, and offers a few additional likely symptoms.
I suspect it took a while to realize the possible adverse reactions associated with E-cigarettes because nobody thought of asking people who turned up at the doctors’ if they were using E-cigarettes. Additionally, the CDC reports that it can take weeks and sometimes longer for symptoms to develop.
Late-appearing symptoms and ones that might not immediately seem obvious to a doctor are exactly the kinds of symptoms that people’s search engine queries are good at detecting. Thus, I turned to search data to see what it might show.
I extracted 9 months (October 2018 – June 2019) of Bing search data. I chose this period of time because it was well before information of the new pulmonary disease were widely reported in the media. These data include searches by people in the United States. Each record comprises of the text of the search, it’s time and date, and an anonymous user identifier.
To analyze the data I followed the methodology Evgeniy Gabrilovich and I developed for our paper on pharmacovigilance, which showed that it was possible to discover new side effects of drugs from search data. Specifically, I filtered the data to focus on those users who mentioned E-cigarette products. My list comprised of general terms related to electronic cigarettes and vaporizers, as well as the brand names of popular E-cigarettes. Although not everyone who mentioned an E-cigarette in their queries uses them, our experience with other product suggests that many who mentioned them are users. Approximately half a million users mentioned these products in their queries during the data period.
I then found all mentions of one of 195 medical symptoms that these users made before or after the first time they queried for an E-cigarette product. As a control population I found all the users who mentioned symptoms in their queries but did not mention an E-cigarette product. For those users I picked a random reference date between their first and last query in our data. I also removed topical queries (which spiked for a few days and then disappeared) and popular queries that were obviously unrelated to medical symptoms. These include, for example, queries mentioning celebrities and their medical issues.
I then scored each symptom using QLRS statistics (see our 2013 paper). Briefly stated, a symptom will receive a high score if we saw a significant rise in the likelihood that it will be queried in the population that also queried for E-cigarettes after their first mention of the product, compared to the control population.
The symptoms that
received the highest scores are shown in the table below. Notice that among the top 10 symptoms at least 4 are also mentioned in the
CDC report. The Top 3
are all known symptoms.
in CDC report
Incidentally, some of the other symptoms reported by CDC are ranked high, though not in the top 10. For example, diarrhea is ranked 12th.
The temporal profile of symptom mentions seems to support the CDC report. In the figure below I plotted the likelihood that a person in the E-cigarette population would ask about cough over time, normalized by the likelihood of asking about cough in the control population. According to this figure, in the first few days after the first mention of an E-cigarette product, cough is slightly less likely and than in the general population. However, within a few weeks, cough becomes more prominent, to the point that it’s about 20% more likely than in the control population.
Given these findings I would suggest that search query data shows the traces of this mysterious new pulmonary disease, recently reported by CDC.
These results also suggest the people that researchers should investigate the possibility of additional adverse effects of E-cigarette use including depression, anxiety, and perspiration.
Crowdsourced Health is a research project which aims to learn about health and medicine from online data. The latter include search engine queries, social media, and other online data.
The output of the project is published in academic papers, listed here. Each paper I’ve published is accommodated by a social media post describing the paper, for people who prefer not to read the more lengthy paper.
During our work we often have findings that are interesting, but perhaps not worth an entire paper. That’s why I’m going to try a new publication format, through short, concise blog posts. I hope these will be interesting for you.