Feedspot: RSS Reader for Google Reader Diaspora

Feedspot: A New RSS Reader

Feedspot: A New RSS Reader

There was plenty of hand-wringing when Google announced that it was ceasing support for Google Reader. As is somewhat typical with Google’s project kills, it was a relatively precipitous decision that had analysts scratching their heads and users scurrying for alternatives.

Feedspot is one of several browser-based Really Simple Syndication (RSS) readers that offers features similar to Google Reader, and — this was critical for many users — includes Outline Processor Markup Language (OPML) import. OPML was the format used to export RSS feeds from Google Reader, as well as for other applications, including the venerable Microsoft Outlook.

As one of the refugees from Google Reader, I had many feeds spanning several engagements since 2005, I had a modest set of hierarchically structured feeds. Some of the folders were quite deep — e.g., “MusicTech” had 77 feeds, while others had just a few. I was a fairly energetic user of folders for structuring as well, and it is easy to imagine that others had more extensive lists. In total, there were around 750 RSS feeds. These were successfully imported into Feedspot in around 20 minutes in the middle of the day EST (US).

Feedspot has the feel of a relatively mature application. The small touches are part of this impression, such as the “tooltips.” An example is shown in the screenshot: “Unread article: A blue triangle in the top left corner indicates unread article. . .”  Other examples include the list-view toggle and the rightmost “Feedback” button leading to a UserVoice dialog. Feedspot also allows building of RSS collections — essentially, feeds of feeds, at the level of folders.


List View Toggle


UserVoice Feedback

A so-called “modern” application must integrate numerous sharing opportunities, and Feedspot follows this trend. Features for sharing content are provided in several spots [sic]. Following of user collections is possible (“follow all of my stuff”) and the obvious — sharing of individual articles.

There are still some rough edges here and there. It was unclear how to place a new manually entered feed, place it into a folder, or how to create more than a 2 level hierarchy if that is supported.

RSS content is sufficiently important that I have placed my post-Reader bets on more than one pony.  Feedspot is one of them. It deserves serious consideration as a Google Reader replacement.

Founder on the Spot

I corresponded with FeedSpot founder Agarwal about the Company. He disclosed that FeedSpot was built using agile methods, and that it runs on a LAMP stack. His business goal? He says he always wanted to build “a consumer internet product.”  Why RSS? Because, he wrote, “for some users an RSS reader is a must-have product.”  But Agarwal believes it is possible to “take an RSS reader to mainstream consumers.”

Future of RSS?

The cancellation of Google Reader had some pundits predicting the end of RSS. Some consider the Facebook-dominated landscape as superceding the lowly RSS. The RSS button, they say, is losing ground to “Follow” and “Like” buttons. Indeed, there is a rich future ahead for the underlying data from those interactions. Still, it safe to say that pronouncements of the death of RSS are premature.  RSS is widely used in content management systems (e.g., Blogger, WordPress, Sharepoint and Joomla) to import links to relevant articles. This is true for externally produced content, of course, but perhaps it has even greater value as a rapid information aggregator for smaller scale intranets whose limited staffing prevents more sophisticated schemes.

RSS, partly because of its UserLand roots,  offers a simple but flexible framework for information management. It doesn’t deliver a directly usable ontology from an automation standpoint, but its wide adoption gives users great leverage to employ ad hoc schemes.


UserVoice Dialog Box

The addition of tagging features in Feedspot strengthens the quasi-ontology capability in RSS. If judiciously used with a controlled vocabulary, search can be more fruitful. As longtime users of Gmail can attest, having to choose between folders and tagging was an uncomfortable either/or decision. Eventually Gmail would offer both for classifying email. Having this capability for managing RSS is helpful.

There is more fertile ground in the reference community, such as RSS feeds that are supported in ResearchGate, CiteULike and academic publishers. If feed schemas become more sophisticated, feed users could deploy them more rapidly. Content publishing based on feeds would more often reach appropriate reader communities. Content delivery at present is notoriously hit-and-miss, especially since bloggers can readily veer off the ostensible topic of the blog.

These are growing pains that have made the semantic web grow so unsteadily. But Feedspot and like tools are perfectly good hammers for the right kind of nail.


Tagging Feature

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Celebrity’s Anonymous Pen Name ‘Outted’ by Software

JGAAP (Java Graphical Authorship Attribution Program)

JGAAP (Java Graphical Authorship Attribution Program)

The role that software plays in stylistic analysis of text is perhaps less surprising to high school and college students than to the general public. The former must submit essays they write to style analysis performed by software which looks for plagiarism and sometimes also makes quality assessments.

In the recent outing of J.K. Rowling as the writer behind the pen name Robert Galbraith, it was mentioned that software had been used to analyze the text of the Galbraith novel.  There exists a family of software used by academics for “authorship attribution,” e.g., to discover, for example, whether a recently discovered manuscript was a missing chapter of Don Quijote (a fabricated example). One of these applications is JGAAP, for Java Graphical Authorship Attribution Program. The JGAAP wiki page explains the project as

. . . Java-based, modular, program for textual analysis, text categorization, and authorship attribution i.e. stylometry / textometry. JGAAP is intended to tackle two different problems, firstly to allow people unfamiliar with machine learning and quantitative analysis the ability to use cutting edge techniques on their text based stylometry / textometry problems, and secondly to act as a framework for testing and comparing the effectiveness of different analytic techniques’ performance on text analysis quickly and easily. JGAAP is developed by the Evaluating Variation in Language Laboratory (EVL Lab) and released under the AGPLv3.

How this was accomplished was explained by one of two academic investigators credited with the analysis (along with some suspicions by reports at the Sunday Times) at . Patrick Juola, in the blog Language Log. Juola refers to this subdiscipline as “forensic stylography.”

A one-paragraph extract from Juola’s blog post follows. Note that, in the usual sense of the word, the analysis doesn’t look directly at “meaning.”

The heart of this analysis, of course, is in the details of the word “compared.” Compared what, specifically, and how, specifically. I actually ran four separate types of analyses focusing on four different linguistic variables. While anything can in theory be an informative variable, my work focuses on variables that are easy to compute and that generate a lot of data from a given passage of language. One variable that I used, for example, is the distribution of word lengths. Each novel has a lot of words, each word has a length, and so one can get a robust vector of <X>% of the words in this document have exactly <Y> letters. Using a distance formula (for the mathematically minded, I used the normalized cosine distance formula instead of the more traditional Euclidean distance you remember from high school), I was able to get a measurement of similarity, with 0.0 being identity and progressively higher numbers being greater dissimilarity.


Cool Socnet Visualization from MIT’s Immersion Project

A previous post considered some practical implications for privacy and government surveillance stemming from the Snowden revelations about the Prism program. The point was made that some people who think they have nothing to hide could easily become ensnared in webs not of their own making, and could find it difficult to untangle themselves.

Interest in metadata patterns in social networks is not limited to the NSA. Prism is one of a number academic, Homeland Security and Department of Defense programs that have studied how to make sense of social communication patterns to identify and track suspects. One of these is MIT’s Immersion project.

Following a tip from Slashdot,  the Immersion project was given the keys to the author’s hyperactive Gmail account (~ inbox = 169,000, 120 filters, 250 labels).  Immersion analyzes a Gmail account without directly accessing one’s Gmail password.

The attached images were produced by Immersion after analyzing 277,843 emails.  As the MIT project team explains,

Once you log in, Immersion will use only the From, To, Cc and Timestamp fields of the emails in the account you are signing in with. It will not access the subject or the body content of any of your emails.

The point? As Slashdot’s “Judgecorp” points out, Immersion gives even a casual observer a sense for what the NSA Prism initiative could do with metadata.

Immersion can also objectively respond to your Mother’s “Why don’t you ever write?” complaint. When used to analyze a single contact, Immersion produces a graph of interactions by year. Also depicted in the screenshots is a plot of the interactions by year.

Yes, writing my sister more often would be a good idea.

As often highlighted at GlitchReporter.com, things in information technology can sometimes go wrong. Spam, misaddressed email, malware or sheer coincidence could put your name on the receiving end of an arrow in an Immersion diagram.

First posted at Port Wash Patch