Tag: RNN

Generation of Realistic Navigation Paths for Web Site Testing using RNNs and GANs

by Marco Brambilla, posted in big data, data science, event, software engineering

Weblogs represent the navigation activity generated by a specific amount of users on a given website. This type of data is fundamental because it contains information on the behaviour of users and how they interface with the company’s product itself (website or application). If a company could have a realistic weblog before the release of its product, it would have a significant advantage because it can use the techniques explained above to see the less navigated web pages or those to put in the foreground.

A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. 

To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs .

Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed.

The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely

  • Recurrent Neural Network, which are very well suited to temporally evolving data
  • Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years.

We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good, as reported in this summary table, with respect to the two evaluation metrics adopted (BLEU and Human evaluation).

Picture1

Comparison of performance of baseline statistical approach, RNN and GAN for generating realistic web logs. Evaluation is done using human assessments and BLEU metrics

 

Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site. and can be cited as:

Pavanetto S., Brambilla M. (2020) Generation of Realistic Navigation Paths for Web Site Testing Using Recurrent Neural Networks and Generative Adversarial Neural Networks. In: Bielikova M., Mikkonen T., Pautasso C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science, vol 12128. Springer, Cham

The slides are online too:

Together with a short presentation video:

 

Predictive Analysis on U.S. Midterm Elections on Twitter with RNN

by Marco Brambilla, posted in data science, social network, social network analysis

We implemented an analysis (meaning both a method and a system) that aim to gauge local support for the two major US political parties in the 68 most competitive House of Representative districts during the 2018 U.S. mid-term elections.

The analysis attempts to mirror the “Generic Ballot” poll, i.e., a survey of voters of a particular district which aims to measure local popularity of national parties by querying participants on the likelihood they would vote for a “generic” Democrat or Republican candidate. We collect the tweets containing national parties and politicians in the 68 most competitive districts. By most competitive we mean that they are rated as: toss up, 50%-50%, or lean by the Cook Political Report.

This means we are addressing an extremely challenging analysis and prediction problem, while disregarding the simpler cases (everyone is good at predicting the obvious!).

Our solution employs the Twitter Search API to query for tweets mentioning a national leader or party, posted form a limited geographic region (i.e., each specific congressional district). For example, the following query extracts tweets on Republicans:

TRUMP OR REPS OR Republicans OR Republican OR MCCCONNELL OR ‘MIKE PENCE’ OR ‘PAUL RYAN’ OR #Republicans OR #REPS OR @realDonaldTrumpOR @SpeakerRyan OR @senatemajldr OR @VP OR GOP OR @POTUS

To limit the search to each congressional district, we use the geocode field in the search query of the API, which queries a circular area based on the coordinates of the center and the radius. Because of the irregular shape of the congressional districts, multiple queries are needed for each of them, therefore we built a custom set of bubbles that approximate the district shape.

For the analysis of the tweets, we adopted a Recurrent Neural Network, namely a RNN-LSTM binary classifier trained on tweets.

To build training and testing data we collected tweets of users with clear political affiliation, including candidates, political activists, and also lesser know users, well versed in the political vernacular.
The accounts selected yielded around 280,000 tweets in 6 months before election day, labeled based on the author’s political affiliation.

Notice that the method is a general political-purpose language-independent analysis framework, that can be applied to any national or local context.

Further details and the results can be found on this Medium post.

This work has been published as a short scientific paper presented at IEEE Big Data Conference in Seattle, WA on December 2018 and on a previous Medium post by Antonio Lopardo.

You can also download a poster format reporting the work:

poster-midterm

In case you want to cite the work, you can do it in this way:

A. Lopardo and M. Brambilla, “Analyzing and Predicting the US Midterm Elections on Twitter with Recurrent Neural Networks,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5389–5391.
doi: 10.1109/BigData.2018.8622441.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8622441&isnumber=8621858

The online running prototype, the full description of the project, its results, and source code are available at http://www.twitterpoliticalsentiment.com/USA/.