In the following, we share the R codes for collecting twitter search network data. This network data is created from tweets containing #BoilerUp hashtag. We create a node for each unique Twitter user who either tweeted one of those tweets, was replied to in one of those tweets, or was mentioned in one of those tweets. These nodes were connected with edges of tweeting, replying, and mentioning. If a user X1 was replied or mentioned by another user X2, the edge between X1 and X2 represents a one way connection from X2 to X1.

First step, you need install package twitterR, igraph, reshape, reshape2 from R. The package graphTweets is not offered within R and one need download it from https://cran.r-project.org/web/packages/graphTweets/index.html to your computer. Then you can install this package from R.

Second step, you need gain the authorization from twitter for your R application. Please read the article here to understand the detailed process. http://thinktostart.com/twitter-authentification-with-r/

Third step, run your codes in R.

library(twitteR)
library(igraph)
library(graphTweets)

#setup_twitter_oauth() function uses the httr package
# replace these value from your register on twitter
#Twitter Authentication with R

Consumer_key=”YOUR API KEY”
Consumer_secret=”YOUR API SECRET”
access_token=”YOUR ACCESS TOKEN”
access_token_secret=”YOUR ACCESS TOKEN SECRET”
setup_twitter_oauth(Consumer_key,Consumer_secret,access_token,access_token_secret)

#pull and parse tweets
tweets=searchTwitter(“#BoilerUp”, n=5000, lang=”en”)
tweets=twListToDF(tweets)
edges=getEdges(data = tweets, tweets = “text”, source = “screenName”)
nodes=getNodes(edges)
graph=graph.data.frame(edges[,1:2], directed=TRUE, vertices=nodes)
write.graph(graph, “D:/SNAdata/boilerup.graphml”, format=”graphml”)

More information about twitterR package can be found from the following link.
# https://cran.r-project.org/web/packages/twitteR/twitteR.pdf

Gephi 0.9.0, Gephi 0.9.1, and Gephi 0.9.2 have been tested on Java 7 and Java 8. Therefore, you are recommended to have these versions of Java installed on your computer.  Check the following website for these versions of Java platforms:

http://www.oracle.com/technetwork/java/javase/downloads/index.html

If you have Java installed, but the Gephi reports that it cannot find the Java platform. One possible reason is that the Gephi installation failed to locate the path of your installed Java platform. You can correct this problem by following these steps:

(1) Open configuration file under the installed folder of your Gephi, for example, C:\Program Files (x86)\Gephi-0.8.2\etc\gephi.conf, depending where you install your Gephi.

(2) Specify jdkhome=”C:\Program Files\Java\jre8″, the specific path you put here depends on where you install your Java platform. Please de-comment out this line by removing the “#” symbol after you change the default specification.

(1) Open the data file in Gephi.

(2) Calculate statistics and size the nodes based on a measurement of centrality.

(3) Partition the network into several communities based on the calculated modularity.

(4) Run a layout algorithm to get a better layout of nodes and edges.

(5) Next, show the labels for nodes and run a label adjust algorithm.

(6) To preview the graph, switch to the “Preview” window. To show the label, you need check the “Show Labels” box.

(7) In Data Laboratory window, you can sort the nodes based on some centrality measurement.

Steps for passing Google Analytics Individual Qualification Test

To prepare for Google Analytics Individual Qualification test, one should first go through Google’s online courses:  https://analyticsacademy.withgoogle.com/course01/course . The course materials include instruction videos and reading articles. The assessment after each unit and the final assessment can be very helpful for you to review what you have learned. However,  the assessment seems much harder than the real test. So please do not be deterred by those assessment questions.

Next, you can take advantage of an online study guide: https://support.google.com/partners/topic/6083717 .

Finally, you can find useful practice questions from this website : http://www.gaiq-center.com/exam-questions-answers/ These practice questions are very helpful, especially if you study the guide carefully.

To start for Google Analytics certification and AdWords certification exam, you should first join in Google partner. Please check this short video for the instructions of joining Google partner.  https://www.youtube.com/watch?v=HNAB9yXoDYw

UConn Professor Asks: Would Reducing Pharmaceutical Sales Calls to Physicians Help or Harm Patients?

When a pharmaceutical company sends a representative to your doctor’s office to promote a new or existing medication, is that a benefit to you as a patient? Would restricting those visits bring greater fairness to the pharmaceutical industry—or prevent your doctor from being well-informed about treatment options?

Those are some of the questions that UConn marketing professor Hongju Liu and three colleagues tackled in a research paper titled, “An Empirical Model of Drug Detailing: Dynamic Competition and Policy Implications” that is pending publication in the journal Management Science.

Hongju Liu “The pharmaceutical industry is tremendously important and has recently undergone significant changes,” Liu said. “What we discovered could have ramifications for physicians, the industry and each of us as patients.”
  -Hongju Liu

The vast amount of detailing—in-person calls by drug-company representatives—in the pharmaceutical industry has drawn interest from the public and legislators. In 2013, the Physician Payment Sunshine Act went into effect, creating transparency requirements resulting in physician practices and hospitals limiting pharmaceutical sales representatives’ access to doctors. Yet there has been little research about how these restrictions impact doctors’ prescription behavior and industry competition, Liu said.

Wide-ranging legislative proposals in different countries have been proposed to regulate and limit detailing activities, and there have also been efforts at self-regulation by both the industry and medical profession. The United Kingdom limits pharmaceutical firms’ sales promotion; Spain prohibits firms from providing more than 10 sample packages to a doctor in one year; and in 2009, Pharmaceutical Research and Manufacturers of America issued new guidelines on marketing to physicians, prohibiting non-educational and practice-related gifts, including pens.

In the U.S., sales representative access to physicians has become increasingly difficult. In 2013, some 45 percent of prescribers put significant restrictions on detailing representative access, compared with just 23 percent five years earlier.

For their research, Liu and his colleagues tracked the prescriptions for three popular statins, including Lipitor (Pfizer), Crestor (AstraZeneca) and Zocor (Merck). Statins are a group of drugs that reduce levels of fats, including triglycerides and cholesterol in the blood. In 2004, when the study began, statin sales surpassed $15.5 billion, making them the biggest-selling drugs in the U.S.

Liu and the other researchers studied 448 physicians who wrote almost 15,000 statin prescriptions and received more than 26,000 detailing visits. The study was based on data from 2002 to 2004 and mid-way through the study period, the new drug, Crestor, was introduced.

Liu and his colleagues wanted to know what the implications were for strategic behaviors as firms compete for the limited number of times they can meet physicians. Which firms gain, and which lose, in terms of revenue and profits? And what are the implications for the industry and public policy?

The researchers drew several conclusions:

By “leveling the playing field,” restrictive policies benefit firms that have the weakest detailing effects, while hurting firms with the strongest. These effects are especially pronounced on prescriptions for new drugs, which need to build up their physician interest. This may be undesirable when new medications are better therapeutically. Imposing a ceiling on detailing frequencies leads to a significant reduction in detailing across the board. A self-imposed restrictive detailing policy established by a single drug firm is not likely to succeed in reducing detailing levels of other firms. With firms’ detailing levels reduced by a restriction policy, the number of non-drug treatment or generic drug prescriptions by medical practitioners expands. But whether that is a benefit or hindrance to public health is undetermined.

Liu collaborated with Qiang Liu, a marketing professor at Purdue University, Sachin Gupta, a professor at Cornell University and Sriram Venkataraman, a professor at the University of North Carolina at Chapel Hill.

Google Analytics offers an easy to use tool (content experiment) for conducting A/B testing. The process of setting up an A/B testing in Google Analytics is kind of straightforward.

(1) In Google Analytics, go to Behavior-> Experiments

(2) Choose an experiment objective:  this can be bounce rate, duration time, and conversion.

(3) Configure your experiment: set up the url for your original page and your experiment page.

(4)  Paste the experiment code immediately after the opening head tag at the top of your original page. Also make sure both of your original page and experiment page contain Google Anlytics track code.

(5) Check and start the experiment.

Read the detailed instructions on setting up an experiment.