Demographic differences in search engine use with implications for cohort selection

Can biased search results change people's opinions about anything at all? a close replication of the Search Engine Manipulation Effect (SEME)

In previous experiments we have conducted on the Search Engine Manipulation Effect (SEME), we have focused on the ability of biased search results to shift voting preferences. In three new experiments with a total of 1,137 US residents (mean age = 33.2), we sought to determine whether biased search rankings could shift people's opinions on topics that do not involve candidates or elections. Each of the new experiments looked at a different topic, and participants were pre-screened to make sure they didn't have strong opinions about these topics. The topics were: Is artificial intelligence useful or dangerous? Is fracking helpful or dangerous? And: Are people born gay or do they choose to be gay? All participants were first asked various demographic questions, then shown brief summaries of the "pro" and "anti" views on each topic, and then asked their opinions about each topic. Next, participants were allowed to conduct an online search using our mock search engine (Kadoodle) lasting up to 15 minutes. In each experiment, one-third of the participants saw biased search results favoring one perspective; one-third saw biased search results favoring the opposing perspective; and one-third (the control group) saw mixed search results. After completing their search, participants were again asked for their opinions about the topic. Our primary dependent variable was Manipulation Power (MP), the percentage increase in the number of participants favoring one viewpoint after having viewed search rankings favoring that viewpoint. The MPs in the three experiments were 25.0%, 30.9%, and 17.8%, respectively. Corresponding shifts were also found for how persuasive participants found each viewpoint to be and for how much they trusted each viewpoint. We conclude that search rankings favoring one viewpoint on a wide range of topics might be able to cause people who have not yet formulated a strong opinion on such topics to adopt the favored perspective. If our findings prove to be robust, we are exposing what might be considered an unforeseen consequence of the creation of search engines, namely that even without human interference, search algorithms will inevitably alter the thinking and behavior of billions of people worldwide on perhaps any topic for which they have not yet formed strong opinions.

Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data

Background

Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension).

Objective

Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event.

Methods

We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions.

Results

The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session.

Conclusions

The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events.

Search Engine Gender Bias

This article discusses possible search engine page rank biases as a consequence of search engine profile information. After describing search engine biases, their causes, and their ethical implications, we present data about the Google search engine (GSE) and DuckDuckGo (DDG) for which only the first uses profile data for the production of page ranks. We analyze 408 search engine screen prints of 102 volunteers (53 male and 49 female) on queries for job search and political participation. For job searches via GSE, we find a bias toward stereotypically "female" jobs for women but also for men, although the bias is significantly stronger for women. For political participation, the bias of GSE is toward more powerful positions. Contrary to our hypothesis, this bias is even stronger for women than for men. Our analysis of DDG does not give statistically significant page rank differences for male and female users. We, therefore, conclude that GSE's personal profiling is not reinforcing a gender stereotype. Although no gender differences in page ranks was found for DDG, DDG usage in general gave a bias toward "male-dominant" vacancies for both men and women. We, therefore, believe that search engine page ranks are not biased by profile ranking algorithms, but that page rank biases may be caused by many other factors in the search engine's value chain. We propose ten search engine bias factors with virtue ethical implications for further research.

Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines: Viewpoint

Recent research has shown the efficacy of screening for serious medical conditions from data collected while people interact with online services. In particular, queries to search engines and the interactions with them were shown to be advantageous for screening a range of conditions including diabetes, several forms of cancer, eating disorders, and depression. These screening abilities offer unique advantages in that they can serve a broad strata of the society, including people in underserved populations and in countries with poor access to medical services. However, these advantages need to be balanced against the potential harm to privacy, autonomy, and nonmaleficence, which are recognized as the cornerstones of ethical medical care. Here, we discuss these opportunities and challenges, both when collecting data to develop online screening services and when deploying them. We offer several solutions that balance the advantages of these services with the ethical challenges they pose.

Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data

Background

Surveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown.

Objective

This study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional.

Methods

We collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions.

Results

On average, 15.49% (1792/12,367, SD 8.4%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=−0.47; P=.08). In addition, worrying symptoms were 14% more likely to be queried about.

Conclusions

Our results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions.

Can internet search engine queries be used to diagnose diabetes? Analysis of archival search data

AIMS

Diabetes is often diagnosed late. This study aimed to assess the possibility for earlier detection of diabetes from search data, using predictive models trained on large-scale data.

METHODS

We extracted all English-language queries made by people in the USA to Bing during 1 year and identified queries containing symptoms of diabetes. We compared the ability of four different prediction models (linear regression, logistic regression, decision tree and random forest) to distinguish between users who stated that they were diagnosed with diabetes and users who did not refer to diabetes or diabetes drugs but queried about at least one of the symptoms.

RESULTS

We identified 11,050 "new diabetes users" who stated they had been diagnosed with diabetes and approximately 11.5 million "control users" who queried about symptoms without querying for terms related to diabetes. Both the logistic regression and the random forest models were able to distinguish between the populations with an area under curve of 0.92 which translates to a positive predictive value of 56% at a false-positive rate of 1%. The model could identify patients up to 240 days before they mentioned being diagnosed.

CONCLUSIONS

Some undiagnosed diabetes patients can be detected accurately according to their symptom queries to a search engine. Such earlier diagnosis, especially in cases of type 1 diabetes, could be clinically meaningful. The ability of search engines to serve as a population-wide screening tool could potentially be improved using additional data provided by users.