Thariq Shihipar – Future of News and Participatory Media

Exploring the chain of truth in explainers

Thariq Shihipar — Wed, 01 Apr 2015 05:01:11 +0000

Starting off my work for the interview assignment where I explored embedding citations in a page, this is the flip-side, how can you make it easy for other people to cite you as a source, e.g. when doing an explainer.

Of particular interest was following the chain of citation, maybe it’s okay to cite a wikipedia article if you see where that wikipedia article is citing itself from. In the webpage, try clicking cite with text in the paragraph with a citation and without.

Chris Borland & Concussions in the NFL

Thariq Shihipar — Wed, 18 Mar 2015 05:41:34 +0000

On Wednesday, March 18th Chris Borland of the San Francisco 49ers retired from professional football, citing fear of future head injuries. This set off a flurry of discussion both on ESPN and Reddit (where it garnered 3000 upvotes, making it one of the most popular threads of the day).

I wanted to investigate how public discussion on the injury and concussions differed from discussion on the Media. To do this, I used a tool I’ve been working on for research inspired by Media Clouds. The tool compares in real time word clouds from Twitter vs AP news sources and tries to highlight differences. The tool is still in BETA so I was unable to give an interactive demo, but here is a screenshot:

On the left hand side is traditional news outlets, on the right is sources from twitter.

News sources seem to be covering Borland’s impressive career, including his college football record at Wisconsin. However, the discussion on Twitter is two-fold, one is expressing shock at the retirement and the second comparing him to Patrick Willis, another linebacker for the 49ers who retired only days earlier due to religious reasons. This aspect of the story is not covered very much in news outlets, but with Willis and Borland gone, 49ers fans have felt like their season has been derailed. Public discussion seems to focus more on the consequences to the 49ers season whereas news outlets are discussing the repercussions to the NFL as a whole.

Toucans & Turtles – A game of League of Legends

Thariq Shihipar — Tue, 24 Feb 2015 02:35:23 +0000

This is a piece reporting on a game of League of Legends (often shorted to League or LoL) on Sunday afternoon featuring Team Solo Mid (TSM) vs Team Liquid streamed live to over 300k viewers. Like most sports, the verbiage is confusing if you’re not a follower, I’ve tried my best to actually report on the game while also including references to help and following the player-based stories.

The following is a brief description of the game itself.

League of Legends is a 5v5 objective-based sport (there is no fixed time per game). Each team picks 5 characters out of a pool of over 100. Games of League can be categorized into 3 phases: early game: where players are pit individually against each other, mid game: where teams start to group and work together, and late game: where objectives and team movements are more important than individual skill.

Teams fight over objectives on the map such as towers or neutral creatures such as the Dragon and Baron. Doing enough damage to an enemy champion results in a ‘kill’ or a short power-play for the other team, during which they can take objectives. Objectives give ‘buffs’ which leads to greater strength for the team that takes them. When the final objective, the enemy’s nexus, is taken the game ends.

The positions for the game are: mid, top, jungle, marksmen and support, with each role playing a unique niche on the team. Each player has a role that they play as well as an in-game nickname, referred to in quotation marks for example, Thariq “trq” Shihipar.

1st placed TSM (8-2) fell in 47 minutes to Team Liquid (5-5) in one of the largest upsets of this season of the League of Legends Championship Series. Highlighted by play from the marksmen Jason “WildTurtle” Tran and Yuri “Keith” Jew, this game was notable for an epic defense as well as highlighting problems with foreign talent in the North American game.

Aspect of the Dragon vs Aspect of the Turtle

Team SoloMid has been notorious this season for giving up early dragons, small objectives that add up over time, in exchange for making plays elsewhere on the map. Normally, TSM is able to use the strength they gain from those plays to contest dragons later in the game, leading to their 8-1 record.

Team Liquid however was able to adequately defend their map during the mid-game[1] while taking dragons, to force a crucial fight at the last dragon. With Team Liquid winning the fight and gaining the “Aspect of the Dragon” buff, which is often known for leading to the end of the game, it seemed like they need only push forward to win.

But in what has been described as “Helms deep-like defense”, TeamSoloMid fought off Liquid as they pushed to their base, with remarkable play from marksman Jason “WildTurtle” Tran, who had one penta-kill, the rarest play in the game, and one quadra-kill in the defense of their base.

WildTurtle gets a pentakill against Team Liquid in a strong, but ultimately failed defense. Pentakills happen on average once every 40 games.

As the score grew even, Team SoloMid finally ventured out of their base, only for Team Liquid to capitalize by rushing to the nexus to the end the game.

Team Liquid closes out the game by going straight for the nexus.

The Great Danes

Team Solo Mid has, aptly, been known for their dominating mid play from Danish prodigy Soren “Bjergsen” Bjerg. This play style often has Bjergsen and jungle and fellow dane Lucas “Santorin” Tao, “the great danes”, working together to get Bjergsen a lead against the enemy mid laner. However, with Bjergsen playing a relatively passive character, the Great Danes were unable to affect the game meaningfully.

This has been a topic of contention for an otherwise immaculate TSM team, can Team Solo Mid win without leaning on their mid laner? WildTurtle’s strong play, despite the loss, has given fans some hope.

The Toucan Has Landed

On the other side, Team Liquid’s new marksman performing admirably in his second start. Yuri “Keith” Jew (also known as The Toucan) was replacing Chae “Piglet” Gwang-jin, a korean player, known for being part of season 3 world champions SKT, arguably one of the best teams in League of Legends history. Piglet has however failed to impress going 2-4 in his starts for the team.

The question of whether team communication or individual skill is more important in League is one that causes large amounts of tension in the community. With teams like WinterFox speaking only Korean, how much longer will the top teams and players in North America be from North America?

In this case, the benching of Piglet for Keith has some breathing a sigh of relief, it seems teams can’t simply export top players from other countries and expect to win. Other korean imports however like Jang-sik “Lustboy” Ham have shown that when well-integrated, foreign talent can bolster a team’s roster, as is often the case in American sports and life.

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MIT has a League of Legends club and a team that represents MIT in intra-collegiate events, more info can be found here: http://web.mit.edu/lolclub/www/

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Game Recap:

Youtube Link: https://www.youtube.com/watch?v=UIkS6-DW9yM

Thariq’s Media Diary

Thariq Shihipar — Wed, 18 Feb 2015 17:40:23 +0000

Hypotheses

I come from an engineering/computer science background, so my interest in this assignment was mapping my news data programatically and doing interesting data visualizations. I’ve learned that data visualization is often easiest if you start with a hypothesis, because then you know what you’re looking for, and even the lack of a result tells you something. So I made two hypotheses about my news reading habits over the past week:

Hypothesis #1: I get most of my news from social sources, such as Facebook & Twitter. The ‘legitimate’ news source I read most often is the New York Times, not because I visit the website, but because the links that are shared tend to be from there. However, all of my news reading is dwarfed by Reddit usage (which is mostly not due to .

Hypothesis #2: A large amount of news that I’ve read has to do with the Chapel Hill tragedy. Being a practicing Muslim and very involved in the community, the story of 3 muslims being killed execution-style in a possible hate crime has dominated my news feed for the past week.

Methods

Empirically, I know that I do almost all of my news reading online and on my computer, so I only analyzed data from my laptop computer.

I installed RescueTime and other existing tracking options, but found that they not useful in tracking the sources of my traffic, especially only for news. Instead, I decided to query my Chrome Search history to be able to trace my activity. This was done using node.js and sqlite3 to make it easily reproducable on other people’s computers if the data is interesting.

The GitHub repo for the code is here: https://github.com/ThariqS/FutureofNews-MediaVisualizations. It is currently poorly documented and doesn’t show you visualizations (only results) but I’m uploading it on GitHub to motivate myself to polish it up.

Results

Finding #1: Sports dominated my history results. 81% of my news-related history entries were sports related from ESPN & Reddit (I read a large amount of e-sports news on Reddit). The nature of the way I read sports news is fundamentally different from how I read traditional news. Sports games change more often than news stories, more time is spent tracking and staying updated (over 80% of my news-related history was for sport stories).

Finding #2: The news sites I visited

Finding #3: The sources of my news could be divided into 4 different areas: (url bar, google, social and other news stories). Interestingly, I found that the largest trigger for me to read news was to be on a news site, many of the articles I read came as branches off news sites I was already on. Google and Twitter were close seconds. Unsurprisingly, little of my news came from me directly going to the urlbar and typing in ‘newyorktimes.com’.

Disappointingly, though I think much of my news is through Facebook, I was unable to get exact numbers on Facebook usage, due to a limitation of the Chrome history storage and how facebook processes links.

Finding #4: Word Cloud/Topic Generation

Discerning the topics of the news articles I read was difficult, given that I only had access to the titles of the pages. Without being able to apply some more advanced natural language processing techniques, I simply made a wordcloud out of the title words. Some key highlights that stick out to me are: “Hackers”, “Isis”, “Senate”, “Muslim”, “Storm”

Further Work & Weaknesses:

I know intuitively that a large amount of my news comes from Facebook, but it appears that this method I used is inaccurate in tracking links originating from Facebook.

Secondly, I would be interested in the news I read through osmosis, i.e. news I see through scrolling through Twitter & Facebook. I may be aware of these headlines even if I don’t know anything more than the introductory blurb for the story.