Document Overview & Resources
This document provides a summary of Netlytic’s purpose and features. Each section provides related resources and links.
Additional Documentation:
- Netlytic Tutorials (Videos and Instructional Guides)
- 1. System Overview
- 2. Text Analysis
- 3. Category (Topical) Analysis
- 4. Network / Visualization Analysis
Introduction
Netlytic is a cloud-based text analyzer and social networks visualizer. Netlytic can automatically summarize large volumes of text and discover and visualize social networks from conversations on social media sites such as Twitter, Youtube, blog comments, online forums and chats. It is designed to help researchers and others to understand an online group’s operation, identify key and influential constituents, and discover how information and other resources flow in a network.
Application
With Netlytic, you can:
- capture (or import) online conversational type data such as tweets, blog comments, forum postings and text messages, etc…(eg…build and collect your own unique dataset or import your own existing dataset)
- find and explore emerging themes of discussions among actors within your dataset,
- automatically build and visualize two different types communication networks: a chain network based on who-replies-to-whom and a personal name network based on who-mentioned-whom. These networks can then be used to discover and explore emerging social connections between actors within online groups.
Audience
Netlytic is ideally suited for analysing online interactions within large online groups and communities such as Twitter, fan/discussion forums, YouTube comments,customer review forums, online classes, health support groups, etc… Specifically, Netlytic can be used to automatically discover what people within an online community are talking about, who is talking to whom, how often they are communicating, the nature of their relationships or interactions (are community members happy, friendly and supportive; or are they angry, hostile and disrespectful to each other) and how strong their relationships are relative to each other. Once discovered, social network information can be used in a myriad of ways such as detecting the presence of an online community, measuring the strength of that community, identifying prominent actors (influential members) and peripheral participants (people who are susceptible to being influenced), identifying and analysing members’ perceptions of products and services, finding popular resources, and sharing information within a network of trust.
Getting Started
Whether you are interested in using Netlytic to exploring social network analysis, for a class project, or examining discussions related to current events, you can begin with just a few easy steps.
Accompanying Resource: Getting Started with Netlytic Guide
Signing in/ Registering
You can sign into the system with OpenID using your existing Gmail or Yahoo! email account (Netlytic will not see your Gmail or Yahoo! password) or alternatively, you can register with another email address.
If you have questions about our tiers or aren’t sure which one to use, check out our Tier Information FAQ section for more details.
Import / Create a dataset
Accompanying Resource: Netlytic Tutorials: Importing
Once you’re logged in, the first step is to create or import a dataset. You can do this by importing an existing dataset or create a new dataset.
Netlytic offers a variety of options the following options for importing and/or creating a dataset, including Twitter, Facebook, YouTube, RSS Feeds, as well as Text (.txt) or .csv files from either your local device or a cloud storage (drop box or google drive) (See Figure 1a)
Figure 1a, Netlytic import option
Below we’ve listed some tips and things to keep in mind when importing from various sources. Netlytic also offers video tutorials and instruction guides for importing.
- Twitter requires to link your Twitter account to use this importer. This importer uses the Twitter REST API v1.1 search/tweets endpoint.
- This returns a collection of relevant Tweets matching a specified query.
- Please note that Twitter's search service and, by extension, the Search API is not meant to be an exhaustive source of Tweets. Not all Tweets will be indexed or made available via the search interface.
- Twitter API rate limit allows about 10 active collectors per user.
- Typically tweets older than a week will not be returned.
Documentation Guide: Data Source: Twitter
- You can use Netlytic to analyze the comments found on youtube video pages. Analysis of the video content is not available.
- Youtube does not require to link your Youtube account to use this importer.
- This importer uses the YouTube Data video comments feed API v2.0
- This option allows to import records using Really Simple Syndication (RSS) feeds.
- This option allows you to import messages from a text or CSV file by uploading files to Netlytic.
- If your dataset includes more than one text file, you will need to upload and import one file at the time..
- Acceptable formats:.
- 1) CSV file (delimiter = a comma; enclosure = a double quotation mark; escape = a backslash). The first line should include columns' names.
- 2) Full-text transcript with the headers:
From: test@gmail.com
Date: Sun, 1 Apr 2007 14:10:17 -0400
Subject: Origin of the term "Internet" ?
In-Reply-To: c8fc@mail.gmail.com
Message-ID: ffff8260@mx.google.com
I would prefer to not have to do it, but each time I try to submit a course paper without it capitalized, I get the paper back marked up by the professors, telling me it is capital I- internet.
Try Communalytic - our new research tool designed to collect, analyze and
visualize publicly available data from Reddit.
Dataset Home Screen
Accompanying Resource: Netlytic Tutorials: Introductory Information
In Netlytic, you can start a recurring collector, so that the system will check for new Twitter or Facebook posts or RSS Feeds updates and automatically add any new messages to your existing dataset. When importing you have the option to select your preferred collection period. Please note each import source varies in terms of its collection period.
Twitter and Facebook collects for 1, 3, 7, 14, or 31 days
RSS Feed – collects for 1, 3, or 6 months
YouTube, Text file, CSV file – one time upload, no option available for collection period.
The data will be collected for the selected time period, after which the collector can either be renewed for another collection period or a new dataset with the same query can be created. The later option makes it easier to compare data.
After you’ve imported your data, you can view all of your datasets under the “My Datasets” tab (See Figure 2). Please see our tutorial resource link above which walks through how to use all of the features on this screen, including:
- Creating a subset based on a date filter (figures 2 & 2b)
- Downloading your dataset as a .csv file
- Editing the original query
- Renewing collection period
- Sharing datasets with collaborators
Figure 2, List of Collected Datasets
Figure 2b, Date filter
Sharing your Dataset
With the share function, if you so choose, you can allow other Netlytic users to view and work with your dataset in their account. From the “My DataSets” tab, click on the “Share” button associated with a given dataset. From there, you can share your dataset with a registered user by adding their email address. (See Figure 13).
Please note: you are sharing the dataset with the email an individual used to register with Netlytic, you are not sharing datasets from Netlytic to an email inbox.
Figure 13, Share options.
Exporting your Dataset
From the “My DataSets” tab, click on the “Export” button to export the datasets created through Netlytic in formats accessible by other popular network analysis software such as Pajek and UCINET. Datasets can also be exported to CSV format from the “My DataSets” tab by selecting the “Export” button beside select datasets.
Account Information
Accompanying Resource: Netlytic Tutorials: Introductory Information
Netlytic offers a three tiered account system. Tier 1 and 2 are great for smaller projects and exploring Netlytic features and are provided free of charge. To upgrade from a Tier 1 to a Tier 2, please click the “Get More” button (See Figure 3).
Figure 3, Request more datasets via “Get More”
If you are working on a larger project that involves extensive data collection Netlytic offers a community-supported Tier 3, with special discounts for educators and students. Figure 4 outlines the differences between account types and storage capacity.
 Tier 1 (Free) | Tier 2 (Free) | Tier 3 (Community-supported) |
|
| Max # of Datasets | 3 | 5 | 100 |
| Max # of Records/Dataset | 2500 | 10000 | 100000 |
| Great for exploring what Netlytic can do! | Great for smaller projects and class assignments! | Great for larger research projects! With the total storage capacity of up to 10M(!) records (100 datasets x 100k records). | |
| This is a default tier | Request a free upgrade by logging in to your account and clicking on the "My Account" page | This tier is no longer available. Please try the Pro version of our new and improved platform for social media researchers at Communalytic |
|---|
Figure 4, Request more datasets via Help Tab
Preview Screen
Accompanying Resource: Netlytic Tutorials: Introductory Information
By clicking on the name of any of the datasets located on the dataset home screen, you will be able to walk through various analysis steps. The first step is to view your dataset in the preview screen (Figure 5). This area is helpful to view the first 1000 records in the dataset to ensure the data collected matches your project topic. If the query has not pulled in relevant material, you can always edit the import and recollect.
The accompanying resource above walked through all of the features available via the preview screen including:
- Filtering the data on the preview screen
- Select which rows of data to display on the preview screen
- Export all the data collected in a .csv file
- View API logs for the dataset
- Link to the clean text (beta) features
Figure 5, Preview step
Text and Network Analysis
The next two steps include text and network analysis, which provide a thorough description of the features and visualizations available while exploring your datasets. Please see our accompanying documentation for further information:
Report Screen
Accompanying Resource: Netlytic Tutorials: Introductory Information
The “Embed” functions, available under the “Step 5 Report” tab, allows users to share various aspects of their work including text analyses, categories, name networks, and chain networks available on their own or others’ websites. When a user click on an “Embed” button, s/he can choose how large the embedded images will appear and will be provided with the HTML code that can then be added to any website. (See Figure 12)
Figure 12, Report, share, and embed options.
References
[1] Caroline Haythornthwaite and Anatoliy Gruzd. “Analyzing Networked Learning Texts.” Proceedings of Networked Learning Conference (2008): 136-143;
[2] Anatoliy Gruzd, “Studying Collaborative Learning Using Name Networks.” Journal of Education for Library and Information Science 50, no. 4 (2009): 243-253;
[3] Anatoliy Gruzd, “Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog,” (conference paper, Internet Research 10.0 Conference, Milwaukee, WI, October 7-11, 2009);
[4] Chung Joo Chung, Anatoliy Gruzd, and Han Woo Park, “Developing an e-Research Tool for Humanities and Social Sciences: Korean Interent Network Miner on Blogosphere,” Journal of Humanities 60, no.12 (2010): 429-446.
[5] Keith N. Hampton, “Internet Use and the Concentration of Disadvantage: Glocalization and the Urban Underclass,”American Behavioral Scientist 53, no. 8 (2010): 1111-1132. http://abs.sagepub.com/content/53/8/1111
[6] Anatoliy Gruzd, Yuri Takhteyev, and Barry Wellman. “Imagining Twitter as an Imagined Community.” American Behavioral Scientist, Special Issue on Imagined Communities 55, no. 10 (2011): 1294-1318.http://abs.sagepub.com/content/55/10/1294
[7] Jennifer Grek Martin, “Two Roads to Middle-earth Converge: Observing Text-based and Film-based Mental Images from TheOneRing.net Online Fan Community.” (master’s thesis, Dalhousie University, 2011).http://dalspace.library.dal.ca/handle/10222/14242.
[8] Martin, J.M.G., Gruzd, A., Howard, V. (2013). Navigating an imagined Middle–earth: Finding and analyzing text–based and film–based mental images of Middle–earth through TheOneRing.net online fan community. First Monday18(5 – 6). DOI: 10.5210%2Ffm.v18i5.4529