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Presentation; Between Platforms and Community: Moderators on Reddit

September 21, 2015

Presentation by intern Nathan Matias on the project he worked on during the summer at the SMC. He has continued to work on his research, so in case you have not read it here is a more updated post on his work:

Followup: 10 Factors Predicting Participation in the Reddit Blackout. Building Statistical Models of Online Behavior through Qualitative Research

Below is the presentation he did for MSR earlier this month:


(Part 2)

(Part 3)

(Part 4)

Co-creation and Algorithmic Self-Determination: A study of player feedback on game analytics in EVE Online

September 16, 2015

We are happy to share SMC’s intern Aleena Chia’s presentation of her summer project titled “Co-creation and Algorithmic Self-Determination: A study of player feedback on game analytics in EVE Online”.  

Aleena’s project summary and the videos of her presentation below:

Digital games are always already information systems designed to respond to players’ inputs with meaningful feedback (Salen and Zimmerman 2004). These feedback loops constitute a form of algorithmic surveillance that have been repurposed by online game companies to gather information about player behavior for consumer research (O’Donnell 2014). Research on player behavior gathered from game clients constitutes a branch of consumer research known as game analytics (Seif et al 2013).[1] In conjunction with established channels of customer feedback such as player forums, surveys, polls, and focus groups, game analytics informs companies’ adjustments and augmentations to their games (Kline et al 2005). EVE Online is a Massively Multiplayer Online Game (MMOG) that uses these research methods in a distinct configuration. The game’s developers assemble a democratically elected council of players tasked with the filtration of player interests from forums to inform their (1) agenda setting and (2) contextualization of game analytics in the planning and implementation of adjustments and augmentations.

This study investigates the council’s agenda setting and contextualization functions as a form of co-creation that draws players into processes of game development, as interlocutors in consumer research. This contrasts with forms of co-creation that emphasize consumers’ contributions to the production and circulation of media content and experiences (Banks 2013). By qualitatively analyzing meeting minutes between EVE Online’s player council and developers over seven years, this study suggests that co-creative consumer research draws from imaginaries of player governance caught between the twin desires of corporate efficiency and democratic efficacy. These desires are darned together through a quantitative public sphere (Peters 2001) that is enabled and eclipsed by game analytics. In other words, algorithmic techniques facilitate collective self-knowledge that players seek for co-creative deliberation; these same techniques also short circuit deliberation through claims of neutrality, immediacy, and efficiency.

The significance of this study lies in its analysis of a consumer public’s (Arvidsson 2013) ambivalent struggle for algorithmic self-determination – the determination by users through deliberative means of how their aggregated acts should be translated by algorithms into collective will. This is not primarily a struggle of consumers against corporations; nor of political principles against capitalist imperatives; nor of aggregated numbers against individual voices. It is a struggle within communicative democracy for efficiency and efficacy (Anderson 2011). It is also a struggle for communicative democracy within corporate enclosures. These struggles grind on productive contradictions that fuel the co-creative enterprise. However, while the founding vision of co-creation gestured towards a win-win state, this analysis concludes that algorithmic self-determination prioritizes efficacy over efficiency, process over product. These commitments are best served by media companies oriented towards user retention rather than recruitment, business sustainability rather than growth, and that are flexible enough to slow down their co-creative processes.

[1] Seif et al (2013) maintain that player behavior data is an important component of game analytics, which includes the statistical analysis, predictive modeling, optimization, and forecasting of all forms of data for decision making in game development. Other data include revenue, technical performance, and organizational process metrics.

(Video 1)

(Video 2)

(Video 3)

(Video 4)

Followup: 10 Factors Predicting Participation in the Reddit Blackout. Building Statistical Models of Online Behavior through Qualitative Research

September 14, 2015

Three weeks ago, I shared dataviz and statistical models predicting participation in the Reddit Blackout in July 2015. Since then, many moderators have offered feedback and new ideas for the data analysis, alongside their own stories. Earlier today, I shared this update with redditors.

UPDATE, Sept 16, 9pm ET: Redditors brilliantly spotted an important gap in my dataset and worked with me to resolve it. After taking the post down for two days, I am posting the corrected results. Thanks to their quick work, the graphics and findings in this post are more robust.

This July, moderators of 2,278 subreddits joined a “blackout,” demanding better communication and improved moderator tools. As part of my wider research on the work and position of moderators in online communities, I have also been asking the question: who joined the July blackout, and what made some moderators and subs more likely to participate?

Reddit Moderator Network July 2015, including NSFW Subs, with Networks labeled

Academic research on the work of moderators would expect that the most important predictor of blackout participation would be the workload, which creates common needs across subs. Aaron Shaw and Benjamin Mako Hill argue, based on evidence from Wikia, that as the work of moderating becomes more complex within a community, moderators grow in their own sense of common identity and common needs as distinct from their community (read Shaw and Hill’s Wikia paper here). Postigo argues something similar in terms of moderators’ relationship to a platform: when moderators feel like they’re doing huge amounts of work for a company that’s not treating them well, they can develop common interests and push back (read my summary of Postigo’s AOL paper here).

Testing Redditors’ Explanations of The Blackout

After posting an initial data analysis to reddit three weeks ago, dozens of moderators generously contacted me with comments and offers to let me interview them. In this post, I test hypotheses straight from redditors’ explanations of what led different subreddits to join the blackout. By putting all of these hypotheses into one model, we can see how important they were across reddit, beyond any single sub. (see my previous post) (learn more about my research ethics and my promises to redditors)


  • Subs who shared mods with other blackout subs were more likely to join the blackout, but controlling for that:
  • Default subs were more likely to join the blackout
  • NSFW subs were more likely to join the blackout
  • Subs with more moderators were slightly more likely to join the blackout
  • More active subs were more likely to join the blackout
  • More isolated subs were less likely to join the blackout
  • Subs whose mods participate in metareddits were more likely to join the blackout
  • Subs whose mods get and give help in moderator-specific subs were no more or less likely to join the blackout

In my research I have read over a thousand reddit threads, interviewed over a dozen moderators, archived discussions in hundreds of subreddits, and collected data from the reddit API— starting before the blackout. Special thanks to everyone who has spoken with me and shared data.

Improving the Blackout Dataset With Comment Data

Based on conversations with redditors, I collected more data:

  • Instead of the top 20,000 subreddits by subscribers, I now focus on the top subreddits by number of comments in June 2015, thanks to a comment dataset collected by /u/Stuck_In_the_Matrix
  • I updated my /u/GoldenSights amageddon dataset to include 400 additional subs, after feedback from redditors on /r/TheoryOfReddit
  • I include “NSFW” subreddits intended for people over 18
  • I account for more bots thanks to redditor feedback
  • I account for changes in subreddit leadership (with some gaps for subreddits that have experienced substantial leadership changes since July) In this dataset, half of the 10 most active subs joined the blackout, 24% of the 100 most active, 14.2% of the 1,000 most active, and 4.7% of the 20,000 most active subreddits.

To illustrate the data, here are two charts of the top 52,754 most active subreddits as they would have stood at the end of June. The font size and node size are related to the log-transformed number of comments from June. Ties between subreddits represent shared moderators. The charts are laid out using the ForceAtlas2 layout on Gephi, which has separated out some of the more prominent subreddit networks, including the ImaginaryNetwork, the “SFW Porn” Network, and several NSFW networks (I’ve circled notable networks in the network graph at the top of this post).

Reddit Blackout July 2015: Top 20,000 Subreddits by comments

Redditors’ Explanations Of Blackout Participation

With 2,278 subreddits joining the blackout, redditors have many theories for what experiences and factors led subs to join the blackout. In the following section, I share these theories and then test one big logistic regression model that accounts for all of the theories together. In these tests, I consider 52,745 subreddits that had at least one comment in June 2015. A total of 1,342 of these subreddits joined the blackout.

The idea of blacking out had come up before. According to one moderator, blacking out was first discussed by moderators three years ago as a way to protest Gawker’s choice to publish details unmasking a reddit moderator. Although some subs banned Gawker URLs from being posted to their communities, the blackout didn’t take off. While some individual subreddits have blacked out in the intervening years, this was the first time that many subs joined together.

I tested these hypotheses with the set of (firth) logistic regression models shown below. The final model (on the right) offers the best fit of all the models, with a McFadden R2 of 0.123, which is pretty good.

Preliminary logistic regression results, J. Nathan Matias, Microsoft Research
Published on September 14, 2015
More info about this research:
Contact: /u/natematias

N = top 52,745 subreddits in terms of June 2015 comments, including NSFW, for subreddits still available on July 2
Comment dataset:
List of subreddits "going private": 
Moderator network queried in June 2015, with gap filling in July 2015 and September 2015

                                                                  Dependent variable:                             
                                         (1)        (2)        (3)        (4)        (5)        (6)        (7)    
default sub                             3.161***   1.065***   1.070***   0.814**    0.720**    0.693**    0.705**  
                                       (0.294)    (0.305)    (0.317)    (0.336)    (0.337)    (0.337)    (0.339)  
NSFW sub                                0.179*     0.235**    0.268***   0.291***   0.288***   0.314***   0.313*** 
                                       (0.098)    (0.099)    (0.099)    (0.101)    (0.101)    (0.102)    (0.102)  
log(comments in june 2015)                         0.263***   0.268***   0.246***   0.258***   0.256***   0.257*** 
                                                  (0.009)    (0.010)    (0.011)    (0.011)    (0.011)    (0.011)  
moderator count                                               0.066***   0.055***   0.053***   0.051***   0.051*** 
                                                             (0.007)    (0.008)    (0.008)    (0.008)    (0.008)  
log(comments):moderator count                                -0.006***  -0.005***  -0.005***  -0.004***  -0.004*** 
                                                             (0.001)    (0.001)    (0.001)    (0.001)    (0.001)  
log(mod roles in other subs)                                            -0.293***  -0.328***  -0.334***  -0.332*** 
                                                                        (0.033)    (0.033)    (0.033)    (0.033)  
log(mod roles in blackout subs)                                          2.163***   2.134***   2.134***   2.133*** 
                                                                        (0.096)    (0.096)    (0.096)    (0.096)  
log(mod roles in other subs):log(mod roles in blackout subs)            -0.255***  -0.248***  -0.254***  -0.254*** 
                                                                        (0.017)    (0.017)    (0.017)    (0.017)  

log(sub isolation, by comments)                                                    -2.608***  -2.568***  -2.569*** 
                                                                                   (0.347)    (0.345)    (0.345)  
log(metareddit participation per mod in june 2015)                                             0.100***   0.103*** 
                                                                                              (0.036)    (0.036)  
log(mod-specific sub participation per mod in june 2015)                                                 -0.024  
Constant                               -3.608***  -4.517***  -4.677***  -4.655***  -4.467***  -4.469***  -4.469*** 
                                       (0.028)    (0.050)    (0.054)    (0.058)    (0.060)    (0.060)    (0.060)  
Observations                            52,745     52,745     52,745     52,745     52,745     52,745     52,745  
Log Likelihood                        -6,520.505 -6,171.874 -6,130.725 -5,861.099 -5,806.916 -5,803.188 -5,803.098
Akaike Inf. Crit.                     13,047.010 12,351.750 12,273.450 11,740.200 11,633.830 11,628.380 11,630.200
Note:                                                                                  *p<0.1; **p<0.05; ***p


The network of moderators who moderate blackout subs is the strongest predictor in this model. At a basic level, it makes sense that moderators who participated in the blackout in one subreddit might participate in another. Making sense of this kind of network relationship is a hard problem in network science, and this model doesn’t include time as a dimension, so we don’t consider which subs went dark before which others. If I had data on the time that subreddits went dark, it might be possible to better research this interesting question, like Bogdan State and Lada Adamic did with their paper on the Facebook equality meme.

Hypothesis 1: Default subs were more likely to join the blackout

In interviews, some moderators pointed out that “most of the conversation about the blackout first took place in the default mod irc channel.” Moderators of top subs described the blackout as mostly an issue concerning default or top subreddits.

This hypothesis supported in the final model. For example, while a non-default subreddit with 4 million monthly comments had a 32.9% chance of joining the blackout (holding all else at their means), a default subreddit of the same size had a 48.6% chance of joining the blackout, on average in the population of subs.

Hypothesis 2: Subs with more comment activity were more likely to join the blackout

Moderators of large, non-default subreddits also had plenty of reasons to join the blackout, either because they also shared the need for better moderating tools, or because they had more common contact and sympathy with other moderators as a group.

Even among subreddits that declined to joint the blackout, many moderators described feeling obligated to make a decision one way or an other. This surprised moderators of large subreddits, who saw it as an issue for larger groups. Size was a key issue in the hundreds of smaller groups that discussed the possibility, with many wondering if they had much in common with larger subs, or whether blacking out their smaller sub would make any kind of dent in reddit’s advertising revenue.

In the final model, larger subs were more likely to join the blackout, a logarithmic relationship that is mediated by the number of moderators. When we set everything else to its mean, we can observe how this looks for subs of different sizes. In the 50th percentile, subreddits with 6 comments per month had a 1.6% chance of joining the blackout — a number that adds up with so many small subs. In the 75th percentile, subs with 46 comments a month had a 2.5% chance of joining the blackout. Subs with 1,000 comments a month had a 5.4% chance of joining, while subs with 100,000 comments a month had a 15.8% chance of joining the blackout, on average in the population of subs, holding all else constan.

Hypothesis 3: NSFW subs were more likely to join the blackout

In interviews, some moderators said that they declined to join the blackout because they saw it as something associated with support for hate speech subreddits taken down by the company in June or other parts of reddit they preferred not to be associated with. Default moderators denied this flatly, describing the lengths they went to dissociate from hate speech communities and sentiment against then-CEO Ellen Pao. Nevertheless, many journalists drew this connection, and moderators were worried that they might become associated with those subs despite their efforts.

Another possibility is that NSFW subs have to do more work to maintain subs that offer high quality NSFW conversations without crossing lines set by reddit and the law. Perhaps NSFW subs just have more work, so they were more likely to see the need for better tools and support from reddit.

In the final model, NSFW subs were more likely to join the blackout than non-NSFW subs. For example, while a non-default, non-NSFW subreddit with 22,800 of comments had a 11.4% chance of joining the blackout (holding all else at their means), an NSFW subreddit of the same size had a 15.3% chance of joining the blackout, on average in the population of subs. Among less popular subs, a non-NSFW sub with 1,000 comments per month had a 5.4% chance of joining the blackout, while an NSFW sub of the same size had a 7.5% chance of joining, holding all else constant, on average in the population of subs.

Hypothesis 4: More isolated subs were less likely to join the blackout

In the interviews I conducted, as well as the 90 or so interviews I read on /r/subredditoftheday, moderators often contrasted their communities with “the rest of reddit.” When I asked one moderator of a support-oriented subreddit about the blackout, they mentioned that “a lot of the users didn’t really identify with the rest of reddit.” Subscribers voted against the blackout, describing it as “a movement we didn’t identify with,” this moderator said.

To test hypotheses about more isolated subs, I parsed all comments in every public subreddit in June 2015, generating an “in/out” ratio. This ratio consists of the total comments within the sub divided by the total comments made elsewhere by the sub’s commenters. A subreddit whose users stayed in one sub would have a ratio above 1, while a subreddit whose users commented widely would have a ratio below 1. I tested other measures, such as the average of per-user in/out ratios, but the overall in/out ratio seems the best.

In the final model, more isolated subs were less likely to join the blackout, on a logarithmic scale. Most subreddit’s commenters participate actively elsewhere on reddit, at a mean in/out ratio of 0.24. That means that on average, a subreddit’s participants make 4 times more comments outside a sub than within it. At this level, holding everything else at their means, a subreddit with 1,000 comments a month had a 4.0% chance of joining the blackout. A similarly-sized subreddit whose users made half of their comments within the sub (in/out ratio of 1.0) had just a 1% chance of joining the blackout. Very isolated subs whose users commented twice as much in-sub had a 0.3% chance of joining the blackout, on average in the population of subs, holding all else constant.

Hypothesis 5: Subs with more moderators were more likely to join the blackout

This one was my hypothesis, based on a variety of interview details. Subs with more moderators tend to have more complex arrangements for moderating and tend to encounter limitations in mod tools. Sums with more mods also have more people around, so their chances of spotting the blackout in time to participate was also probably higher. On the other hand, subs with more activity tend to have more moderators, so it’s important to control for the relationship between mod count and sub activity.

I was wrong. In the final model, subs with more moderators were LESS likely to join the blackout. There is a very small relationship here, and the relationship is mediated by the number of comments. For a sub with 1000 comments per month, with everything else at its average, a subreddit with 3 moderators (the average) had 5.4% chance of joining the blackout. A subreddit with 8 moderators had a 6% chance of joining the blackout, on average in the population of subs.

Hypothesis 6: Subs with admins as mods were more (or less) likely to join the blackout

I heard several theories about admins. During the blackout, some redditors claimed that admins were preventing subs from going private. In interviews, moderators tended to voice the opposite opinion. They argued that subs with admin contact were joining the blackout in order to send a message to the company, urging it to pay more attention to employees who advocated for moderator interests. Moderators at smaller subs said, “we felt 100% independent from admin assistance so it really wasn’t our fight.”

None of my hypothesis tests showed any statistically significant relationship between current or past admin roles as moderators and participation in the blackout, either way. For that reason, I omit it from my final model.

Hypothesis 7: Subs with moderators who moderated other subs were more likely to join the blackout

I’ve been wondering if moderators with multiple mod roles elsewhere on reddit would be more likely to join the blackout, perhaps because they had greater “solidarity” with other subreddits, or because they were more likely to find out about the blackout.

In the final model, the reverse is supported. Subs that shared moderators with other subs were actually less likely to join the blackout, a relationship that is mediated by the by the number of moderators who also modded blackout subs. Holding blackout sub participation constant, a sub of 1,000 comments per month and 3 moderator roles shared with other subs had a 5.7% chance of joining the blackout, while a more connected sub with 6 shared moderator roles (in the 4th quantile) had a 4.2% chance of joining the blackout, on average in the population of subs, holding all else constant.

Hypothesis 8: Subreddits with mods who also moderate other blackout subs were more likely to join the blackout.

This hypothesis is also a carry-over from my previous analysis, where I found a statistically-significant relationship. Note that making sense of this kind of network relationship is a hard problem in network science, and that we can’t use this to test “influence.”

In the final model, subreddits with mods with roles in other blackout subs were more likely to join the blackout, a relationship on a log scale that is mediated by the number of moderator roles shared with other subs more generally. 19% of subs in the sample share at least one moderator with a blackout sub, after removing moderator bots. A sub with 1,000 comments per month that didn’t have any overlapping moderators with blackout subs had a 3.2% chance of joining the blackout, while a sub with one overlapping moderator had an 11.1% chance to join, and a sub with 2 overlapping moderators had a 21.1% chance to join. For a sub with 6 overlapping moderators with blackout subs, a sub had a 57.2% chance of joining the blackout.

I tend to see the network of co-moderation as a control variable. We can expect that moderators who joined the blackout would be likely to support it across the many subs they moderate. By accounting for this in the model, we get a clearer picture on the other factors that were important.

Hypothesis 9: Subs with moderators who participate in metareddits were more likely to join the blackout

In interviews, several moderators described learning about the blackout from “meta-reddits” which cover major events on the site, and which mostly stayed up during the blackout. Just like we might expect more isolated subs to stay out of the blackout, we might expect moderators who get involved in reddit-wide meta-discussion to join the blackout. I took my list of metareddits from this TheoryOfReddit wiki post.

In the final model, subs with moderators who participate in metareddits were more likely to join the blackout, on a logarithmic scale. Most moderators on the site do not participate in metareddits. A sub of 1,000 comments per month with no metareddit participation by its moderators had a 5.3% chance of joining the blackout, while a similar sub whose moderators made 5 comments on any metareddit per month had a 6.3% chance of joining the blackout.

Hypothesis 10: Subs with mods participating in moderator-focused subs were more likely to join the blackout

Although key moderator subs like /r/defaultmods and /r/modtalk are private and inaccessible to me, I could still test a “solidarity” theory. Perhaps moderators who participate in mod-specific subs, who have helped and been helped by other mods, would be more likely to join the blackout?

Although this predictor is significant in a single-covariate model, when you account for all of the other factors, mod participation in moderator-focused subs is not a significant predictor of participation in the blackout.

This surprises me. I wonder: since moderator-specific subs tend to have low volume, one month of comments may just not be enough to get a good sense of which moderators participate in those subs. Also, this dataset doesn’t include IRC discussions (nor will it ever), where moderators seem mostly to hang out with and help each other. But from the evidence I have, it looks like help from moderator-focused subs played no part to sway moderators to join the blackout.

So, how DID solidarity develop in the blackout?

The question is still open, but from these statistical models, it seems clear that factors beyond moderator workload had a big role to play, even when controlling for mods of multiple subs that joined the blackout.

In further analysis in the next week, I’m hoping to include:

  • Activity by mods in each sub (comments, deletions)
  • Comment karma, as another measure of activity (still making sense of the numbers to see if they are useful here)
  • The complexity of the subreddit, as measured by things in the sidebar (possibly)

Building Statistical Models of Online Behavior through Qualitative Research

The process of collaborating with redditors on my statistical models has been wonderful. As I continue this work, I’m starting to think more and more about the idea of participatory hypothesis testing, in parallel with work we do at MIT around a Freire-inflected practices of “popular data“. If you’ve seen other examples of this kind of thing, do send them my way!

Wired picks SMC blog

September 14, 2015

Wired recently selected its 21 “must-follow” feeds in the world of business, and the Social Media Collective blog was among them! See the entire list here. We’re thrilled, as so much of our goal is to span both scholarly and industry conversations around social media and its critical cultural implications. Stay tuned for more from this blog in the coming months.

Moderator Solidarity on Reddit: Predicting Participation in the Blackout of July 2015

August 25, 2015

For the last 40 years or more, online platforms have relied on people to facilitate and support our online communities. In the early 70s, they were the librarians and shopkeepers of Community Memory. In the 80s, they were the WELL’s “conference hosts.” In the 90s, they were AOL’s “community leaders.” In 2015, they are Wikipedia’s “administrators,” Facebook’s “admins,” Slashdot’s “moderators,” or XBOX’s “enforcement united.” And on platforms like Twitter without moderators, we find the need to invent them. These moderators are the founders, designers, promoters, facilitators recruiters, legislators, responders, and enforcers of our online social interactions.

This summer, I’ve been doing qualitative research on ways that Reddit moderators develop common interests as they face the company, as they face their subscribers, and as they relate to other moderators. Just in the top 20,000 subreddits by subscribers, Reddit has 50,790 moderators. This July, moderators of 2,278 subreddits joined a “blackout,” demanding better communication and improved moderator tools. The blackout is one moment in the wider research I’m doing, a moment where tensions and common cause rose to the surface. Blacked-out subreddits constituted 60% of the top 10 subreddits, 29% of the top 100, and 5% of the top 20,000 subreddits, representing a total of 134.8 million combined subscriptions.

Since I can only get so far by reading Reddit threads, I’m now interviewing Reddit moderators to learn more about your experience as a moderator and your experience of the blackout. If you are interested to talk, please message me on Reddit at /u/natematias.

Work In Progress: Charting the Reddit Blackout of July 2015

Since I’m also a software engineer and quantitative researcher, I’ve been complementing my qualitative work with data analysis on what I was able to collect from the public API, combined with /u/GoldTesting’s dataset of blackout participation. Mostly, I’ve used that data to decide where to look and who to reach out to. The conversations I found led me to think about several hypotheses I could also test statistically:

When moderators discussed the blackout with their subscribers, many debated the idea of “solidarity,” wondering if they were too small to have common cause with larger subs or if they were too small to make a difference. Others expressed strong opinions that joining the blackout meant standing with other moderators or standing for Reddit users as a whole.

The conversations I found led me to think about several hypotheses I could test statistically:

H1: Larger subreddits were more likely to join the blackout, maybe because their moderators were part of ModTalk, where much of the blackout was discussed, or because they felt a blackout would make a difference, or because they felt common cause with other mods of large subs.

H2: Subreddits with more moderators were more likely to join the blackout, perhaps mods in these subs would have greater solidarity with others.

H3: Subreddits with mods who also moderate other subreddits that participated in the blackout were more likely to join the blackout

To illustrate the data used for my statistical tests, here are two network graphs of shared moderators between subreddits. The first graph includes the top 20,000 subreddits in terms of subscribers (as of mid-June 2015). The graph one filters only subreddits with more than 10,128 subscribers. In the network graphs, subreddits that did not black out are tinted blue, while yellow-tinted subreddits joined the blackout.

Reddit Blackout July 2015: Top 20,000 Subreddits

Reddit Blackout July 2015: Subreddits with >10,000 Subscribers

The charts are laid out using the ForceAtlas2 layout on Gephi, which has separated out some of the more prominent subreddit networks, including the ImaginaryNetwork, the “SFW Porn” Network, and toward the center, the ShitRedditSays “fempire”. These networks are notable because some of them made network-wide decisions about their participation in the blackout.

Using this dataset, I conducted a logistic regression testing the above hypotheses.

Predicting Participation in the Reddit Blackout, July 2015

H1: Larger subreddits were more likely to join the blackout. This hypothesis is supported. On average in the population of top 20k subreddits, there is a large positive relationship between the log-transformed subscriber count and a subreddit’s probability of joining the blackout, holding all else constant.

H2: Subreddits with more moderators were more likely to join the blackout. This hypothesis is supported, very very weakly. I wouldn’t make much of this.

H3: Subreddits with mods who also moderate other subreddits that participated in the blackout were more likely to join the blackout. This is supported. On average in the top 20,000 subreddits, there is a positive relationship between the log of moderator roles in other blackout subs and a subreddit’s probability of joining the blackout, a relationship that is mediated by the overall number of moderators shared with other subs, holding all else constant.

So, is there evidence of moderator “solidarity” ? Yes, if we consider H1 to be a test of solidarity associated with similar subscriber numbers, and if we consider H2 to be a test of solidarity related to the number of moderators one works together with, then yes, we see support for the solidarity hypothesis. However, my qualitative research shows that many subreddits voted on this issue, indicating that subscribers also matter to this picture. Furthermore, many mods of smaller subs also expressed solidarity, even if smaller subs were less likely to participate. So more work needs to be done.

CAVEATS: This is just a preliminary statistical test. I have much more work to do before publication:

  • I need to define better hypotheses that can answer theoretically-meaningful questions
  • I need to do much more work to confirm the validity of my data collection, data processing, and models
  • I need better definitions of “solidarity”
  • This needs to be peer reviewed

In particular, I plan to spend more time with network scientists to understand the best way to set up my dataset for statistical analysis. There are many ways to project a complex network onto a single table for statistical tests, and I may need to try a different approach. Note also that this model does not include time as a factor, and that I use the term “predict” to refer to statistical inference rather than some ability to predict participation in the blackout before it occurred.

I’m sharing these preliminary results because I hope they’ll attract interest from Reddit moderators, and hopefully lead me to more interviews and data while I still have time to talk to people and enrich my understanding of what happened. If you are a Reddit moderator and want to talk with me, please message me at /u/natematias.

Reading list on the digital divide/digital inclusion

August 13, 2015

The Social Media Collective extended family compiled a bibliography on the digital divide (including gender gaps) and ICTD. Thanks to all of those who contributed!


Allen, Steven G. 2001. “Technology and the Wage Structure.” Journal of Labor Economics 19:440-483.

Anderson, B. 2005. “The value of mixed-method longitudinal panel studies in ICT research.” Information, Communication & Society 8:343-367.

Anderson, Ben. 2008. “The Social Impact of Broadband Household Internet Access.” Information, Communication & Society 11:5-24.

Andrés, Luis, David Cuberes, Mame A. Diouf, and Tomas Serebrisky. 2007. “Diffusion of the Internet: A Cross-Country Analysis.” in World Bank Policy Research Working Paper Series: World Bank.

Attewell, Paul. 2001. “The First and Second Digital Divides.”Sociology of Education.74:252-259.

Attewell, Paul, and Juan Battle. 1999. “Home Computers and School Performance.” Information Society 15:1-10.

Autor, David H. 2001. “Wiring the Labor Market.” The Journal of Economic Perspectives 15:25-40.

Autor, David H., Lawrence F. Katz, and Alan B. Krueger. 1998. “Computing Inequality: Have Computers Changed the Labor Market?” The Quarterly Journal of Economics 113:1169-1213.

Avgerou, C. (2002) Information Systems and Global Diversity, Oxford, Oxford University Press

Barlow, John Perry. 1996. “A Declaration of the Independence of Cyberspace.” Humanist 56(3):18-19.

Barron, B. 2006. “Interest and Self-Sustained Learning as Catalysts of Development: A Learning Ecology Perspective.” Human Development 49:193-224.

Barzilai-Nahon, Karine. 2006. “Gaps and Bits: Conceptualizing Measurements for Digital Divide/s.” Information Society 22:269-278.

Bennett, Sue, Karl Maton, and Lisa Kervin. 2008. “The ‘Digital Natives’ Debate: A Critical Review of the Evidence.” British Journal of Educational Technology 39:775-786.

Billon, Margarita, Rocio Marco, and Fernando Lera-Lopez. 2009. “Disparities in ICT adoption: A multidimensional approach to study the cross-country digital divide.” Telecommunications Policy 33:596-610.

Bimber, Bruce. 2000. “Measuring the gender gap on the Internet.” Social Science Quarterly 81:868-876.

Boase, Jeffrey, John Horrigan, Barry Wellman, and Lee Rainie. 2006. “The Strength of Internet Ties.” Washington, DC.: Pew Internet and American Life Project.

Boeder, P. (2005) Habermas’ heritage: the future of the public sphere in the network society, First Monday 10(9)

Boneva, Bonka S., Robert Kraut, and David Frohlich. 2001. “Using E-Mail for Personal Relationships: The Difference Gender Makes.” American Behavioral Scientist 45:530-49.

Bonfadelli, Heinz. 2002. “The Internet and Knowldege Gaps: A Theoretical and Empirical Investigation.” European Journal of Communication 17:65-84.

boyd, d. 2011. “White Flight in Networked Publics: How Race and Class Shaped American Teen Engagement with MySpace and Facebook.” in Race After the Internet, edited by L. Nakamura and P. Chow-White. New York: Routledge.

Brandtzæg, Petter Bae, Jan Heim, and Amela Karahasanović. 2011. “Understanding the new digital divide—A typology of Internet users in Europe.” International Journal of Human-Computer Studies 69:123-138.

Brynin, M., B. Anderson, and Y. Raban. 2007. “Introduction.” in Information and Communication Technologies in Society: E-living in a Digital Europe, edited by B. Anderson, M. Brynin, J. Gershung, and Y. Raban. London: Routledge.

Buckingham, David. 2007. “Digital Media Literacies: rethinking media education in the age of the Internet.” Research in Comparative and International Education 2:43-55.

Buente, Wayne, and Alice Robbin. 2008. “Trends in Internet information behavior, 2000–2004.” Journal of the American Society for Information Science and Technology 59:1743-1760.

Burrell, Jenna. 2009. “What Constitutes Good ICTD Research?” Information Technologies & International Development 5 (3): 82–94.

Buskens, I. & Webb, A. (Eds.) (2009) African women and ICTs – Investigating Technology, Gender and Empowerment, Pretoria, Unisa, IDRC, Zed Books

Callon, M. (1991). Techno-economic networks and irreversibility. In J. Law, A sociology of monsters: Essays on power, technology and domination. London: Routledge.

Castells, M. (2000) The Information Age: Economy, Society and Culture: The Rise of the Network Society, Oxford, Blackwell.

Chapman, R., Slaymaker, T., & Young, J. (2003), Livelihoods Approaches to Information Communication in Support of Rural Poverty Elimination and Food Security, Overseas Development Institute. Available at

Charness, Neil, and Patricia Holley. 2004. “The New Media and Older Adults: Usable and Useful?” American Behavioral Scientist 48:416-433.

Chen, Wenhong, and Barry Wellman. 2005. Minding the Cyber-gap: the Internet and Social Inequality. Boston, MA: Blackwell Publishing Ltd.

Chou, Wen-ying Sylvia, Yvonne M.  Hunt, Ellen Burke Beckjord, Richard P. Moser, and Bradford W. Hesse. 2009. “Social Media Use in the United States: Implications for Health Communication.” Journal of Medical Internet Research 11:e48.

Compaine, B.M. 2001a. “Information Gaps.” Pp. 105-118 in The Digital Divide: Facing a Crisis or Creating a Myth?, edited by B Compaine. Cambridge, MA: The MIT Press.

Compaine, Benjamin M. (Ed.). 2001b. The Digital Divide: Facing a Crisis or Creating a Myth? Campbridge, MA.: MIT Press.

Cook, Thomas D., Hilary Appleton, Ross F. Conner, Ann Shaffer, Gary Tamkin, and Stephen J. Weber. 1975. “Sesame Street” Revisited. New York: Russel Sage Foundation.

Correa, Teresa. 2010. “The Participation Divide Among “Online Experts”: Experience, Skills and Psychological Factors as Predictors of College Students’ Web Content Creation.” Journal of Computer-Mediated Communication 16(1):71-92.

Crenshaw, Edward M., and Kristopher K. Robison. 2006. “Globalization and the Digital Divide: The Roles of Structural Conduciveness and Global Connection in Internet Diffusion.” Social Science Quarterly (Blackwell Publishing Limited) 87:190-207.

Czaja, Sara J., Neil Charness, Arthur D. Fisk, Christopher Hertzog, Sankaran N. Nair, Wendy A. Rogers, and Joseph Sharit. 2006. “Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (create).” Psychology and Aging 21:333-352.

DFID (1999) Sustainable Livelihoods Guidance Sheets. London, Department for International Development, available at:

Dholakia, Ruby Roy. 2006. “Gender and IT in the Household: Evolving Patterns of Internet Use in the United States.” Information Society 22:231-240.

DiMaggio, Paul , and Eszter  Hargittai. 2001. “From the ‘Digital Divide’ to `Digital Inequality’: Studying Internet Use As Penetration Increases.” Princeton, NJ: Center for Arts and Cultural Policy Studies at Princeton University.

DiMaggio, Paul, and Bart Bonikowski. 2008. “Make Money Surfing the Web? The Impact of Internet Use on the Earnings of US Workers.” American Sociological Review 73:227-250.

DiMaggio, Paul, Eszter Hargittai, Coral Celeste, and Steve Shafer. 2004. “Digital Inequality: From Unequal Access to Differentiated Use.” Pp. 355-400 in Social Inequality, edited by Kathryn Neckerman. New York: Russell Sage.

DiMaggio, Paul, Eszter Hargittai, W. Russell. Neuman, and John P. Robinson. 2001. “Social implications of the Internet.” Annual Review of Sociology 27:307-336.

DiNardo, John E., and Jorn-Steffen Pischke. 1997. “The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?” The Quarterly Journal of Economics 112:291-303.

Donner, Jonathan. 2008. “Research Approaches to Mobile Use in the Developing World: A Review of the Literature.” The Information Society 24 (3): 140–159.

Donner, Jonathan. 2008. The rules of beeping: Exchanging messages via intentional “missed calls” on mobile phones. Journal of Computer-Mediated Communication 13, (1): 1-22.

Drori, Gili S., and Yong Suk Jang. 2003. “The Global Digital Divide: A Sociological Assessment of Trends and Causes.” Social Science Computer Review 21:144-161.

Drori, Gili. S. 2010. “Globalization and Technology Divides: Bifurcation of Policy between the “Digital Divide” and the “Innovation Divide”*.” Sociological Inquiry 80:63-91.

Duncombe R.(2006). Using the livelihoods framework to analyze ICT applications for poverty reduction through microenterprise. Information Technologies and International Development 3(3), 81–100

Dutton, William H., Ellen J. Helsper, and Monica M. Gerber. 2009. “The Internet in Britain 2009.” Oxford: Oxford Internet Institute, University of Oxford.

Dutton, William H., Everett M. Rogers, and Suk-Ho Jun. 1987. “Diffusion and Social Impacts of Personal Computers.” Communication Research 14:219-250.

Dutton, William H., Patrick L. Sweet, and Everett M. Rogers. 1989. “Socioeconomic Status and the Early Diffusion of Personal Computing in the United States.” Social Science Computer Review 7:259-271.

Ellison, Nicole B., Charles Steinfeld, and Cliff Lampe. 2007. “The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites.” Journal of Computer-Mediated Communication 12: article 1.

Entorf, Horst, Michel Gollac, and Francis Kramarz. 1999. “New Technologies, Wages, and Worker Selection.” Journal of Labor Economics 17:464-491.

Eshet-Alkalai, Yoram. 2004. “Digital Literacy: a Conceptual Framework for Survival Skills in the Digital Era.” Journal of Educational Multimedia and Hypermedia 13:93-106.

Ettema, James S., and F. Gerald Kline. 1977. “Deficits, Differences, and Ceilings: Contingent Conditions for Understanding the Knowledge Gap.” Communication Research 4:179-202.

Eurostat. 2008. “Nearly 30% of individuals use internet banking.” Eurostat.

Eurostat. 2009. “One person in two in the EU27 uses the internet daily.” Eurostat.

Eynon, Rebecca. 2009. “Mapping the digital divide in Britain: implications for learning and education.” Learning, Media and Technology 34(4):277-290.

Eynon, Rebecca, and Ellen Helsper. 2011. “Adults Learning Online: Digital Choice and/or Digital Exclusion?” New Media & Society. 13(4):534-51.

Fairlie, Robert W., Daniel O. Beltran, and Kuntal K. Das. 2010. “Home Computers and Educational Outcomes: Evidence from the NLSY97 and CPS.” Economic Inquiry 48:771-792.

Floridi, L. (2009) The Information Society and Its Philosophy: Introduction to the Special Issue on “The Philosophy of Information, Its Nature, and Future Developments”, The Information Society, 25, 153-158.

Forestier, Emmanuel, Jeremy Grace, and Charles Kenny. 2002. “Can Information and Communication Technologies Be Pro-poor?” Telecommunications Policy 26 (11) (December): 623–646.

Forman, Chris. 2005. “The Corporate Digital Divide: Determinants of Internet Adoption.” Management Science 51:641-654.

Forman, Chris, Avi Goldfarb, and Shane Greenstein. 2005. “The Geographic Dispersion of Commercial Internet Use.” Pp. 113-145 in Rethinking Rights and Regulations Institutional Responses to New Communications Technologies, edited by Lorrie Faith Cranor and Steven S. Wildman. Cambridge, MA: MIT Press.

Forman, Chris, Avi Goldfarb, and Shane M. Greenstein. 2009. “The Internet and Local Wages: Convergence or Divergence?” in NBER Working Paper Series. Cambridge, MA: National Bureau of Economic Research.

Fountain, Christine. 2005. “Finding a Job in the Internet Age.” Social Forces 83:1235-1262.

Freedom House (2009) Freedom on the Net: a Global Assessment of Internet and Digital Media. Washington, D.C.: Freedom House

Freese, Jeremy, Salvador Rivas, and Eszter Hargittai. 2006. “Cognitive Ability and Internet Use among Older Adults.” Poetics 34:236-249.

Fuchs, Thomas, and Ludger Woessmann. 2004. “Computers and Student Learning: Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School.” in CESifo Working Paper Series No. 1321. Munich: CESifo Group.

Gaziano, Cecilie. 1983. “The Knowledge Gap: An Analytical Review of Media Effects.” Communication Research 10:447-486.

Goodman, Sy. E., Larry I. Press, S. R. Ruth, and A. M. Rutkowski. 1994. “The Global Diffusion of the Internet: Patterns and Problems.” Communications of the ACM 37:27-31.

Graham, M., S. De Sabbata, and M. A. Zook. (2015) “Towards a Study of Information Geographies: (im)mutable Augmentations and a Mapping of the Geographies of Information.” Geo: Geography and Environment, doi:10.1002/geo2.8. (HTML version here)

Graham, M., De Sabbata, S., Zook, M. 2015. Towards a study of information geographies:(im)mutable augmentations and a mapping of the geographies of information Geo: Geography and Environment. doi:10.1002/geo2.8

Graham, M. 2015. Contradictory Connectivity: Spatial Imaginaries and Techno-Mediated Positionalities in Kenya’s Outsourcing Sector. Environment and Planning A (in press).

Graham, M and De Sabbata, S. 2015 Mapping Information Wealth and Poverty: The Geography of Gazetteers. Environment and Planning A (in press).

Graham, M., Hogan, B., Straumann, R. K., and Medhat, A. 2014. Uneven Geographies of User-Generated Information: Patterns of Increasing Informational Poverty. Annals of the Association of American Geographers. 104(4). 746-764. (pre-publication version here)

Graham, M. 2014 Inequitable Distributions in Internet Geographies: The Global South is Gaining Access But Lags in Local Contentinnovations 9(3-4). 17-34.

Graham, M. 2014. A Critical Perspective on the Potential of the Internet at the Margins of the Global Economy. In Society and the Internet: How Networks of Information and Communication are Changing our Lives. eds. Graham, M., and Dutton, W. H. Oxford: Oxford University Press. 301-318.

Graham, M. and H. Haarstad. 2013. Open Development through Open Consumption: The Internet of Things, User-Generated Content and Economic Transparency. In Open Development: Networked Innovations in International Development. eds. Smith, M. L., and Reilly, K. M. A., Cambridge, MA: MIT Press. 79-111.

Graham, M and M. Zook. 2013. Augmented Realities and Uneven Geographies: Exploring the Geo-linguistic Contours of the Web. Environment and Planning A 45(1) 77-99.

Graham, M. and L. Mann. 2013. Imagining a Silicon Savannah? Technological and Conceptual Connectivity in Kenya’s BPO and Software Development Sectors. Electronic Journal of Information Systems in Developing Countries. 56(2). 1-19.

Graham, M., S. Hale, and M. Stephens. 2012. Digital Divide: The Geography of Internet Access.Environment and Planning A, 44(5)1009-1010.

Graham, M. and H. Haarstad. 2011. Transparency and Development: Ethical Consumption through Web 2.0 and the Internet of ThingsInformation Technologies and International Development. 7(1). 1-18.

Graham, M. 2011. Time Machines and Virtual Portals: The Spatialities of the Digital DivideProgress in Development Studies. 11 (3). 211-227.

Graham, M. 2011. Cultural Brokers, the Internet, and Value Chains. In The Cultural Wealth of Nations. eds. Wherry, F. and N. Bandelj. Standford: Stanford University Press. 222-239.

Graham, M. 2011. Disintermediation, Altered Chains and Altered Geographies: The Internet in the Thai Silk IndustryElectronic Journal of Information Systems in Developing Countries. 45(5), 1-25

Graham, M. 2010. Justifying Virtual Presence in the Thai Silk Industry: Links Between Data and DiscourseInformation Technologies and International Development. 6(4), 57-70.

Graham, M. 2008. Warped Geographies of Development: The Internet and Theories of Economic Development. Geography Compass, 2(3), 771-789.

Greenberg, B., and B. Dervin. 1970. “Mass Communication among the Urban Poor.” Public Opinion Quarterly 34(2):224-235.

Grimshaw, D and Shalini, K. (eds)(2011) Strengthening Rural Livelihoods: The impact of information and communication technologies in Asia, Practical Action Publishing: Rugby

Grusky, David (Ed.). 2008. Social Stratification: Class, Race, and Gender in Sociological Perspective. Boulder, CO: Westview Press.

Grusky, David B., and Manwai Ku. 2008. “Gloom, Doom, and Inequality.” in Social Stratification: Class, Race, and Gender in Sociological Perspective, edited by David B. Grusky. Boulder, CO: Westview Press.

Gui, Marco, and Gianluca Argentin. 2011. “Digital skills of internet natives: Different forms of digital literacy in a random sample of northern Italian high school students.” New Media & Society.

Guillén, Mauro F., and Sandra L. Suárez. 2005. “Explaining the Global Digital Divide: Economic, Political and Sociological Drivers of Cross-National Internet Use.” Social Forces 84:681-708.

Hafkin, N. & Huyer, S. (2007) Women and Gender in Ict Statistics and Indicators for Development. Information Technologies and International Development, 4, 25-41.

Hale, T.M., S.R. Cotten, P. Dremtea, and M. Goldner. 2010. “Rural-Urban Differences in General and Health-Related Internet Use.” American Behavioral Scientist 53:1304-1325.

Halford, Susan, and Mike Savage. 2010. “Reconceptualizing Digital Social Inequality.” Information, Communication & Society 13:937 – 955.

Hampton, Keith N., Lauren F. Sessions, Eun Ja Her, and Lee Rainie. 2009. “Social Isolation and New Technology: How the Internet and Mobile Phones Impact Americans’ Social Networks.” Washington D.C.: Pew Internet and American Life Project.

Hampton, Keith N., and Barry Wellman. 2003. “Neighboring in Netville: How the Internet Supports Community and Social Capital in a Wired Suburb.” City and Community 2:277-311.

Hargittai, Eszter. 2002. “Second-Level Digital Divide: Differences in People’s Online Skills.” in First Monday.7(4)

Hargittai, Eszter. 2011. “Open Doors, Closed Spaces? Differentiated Adoption of Social Network Sites by User Background.” in Race after the Internet, edited by P. Chow-White and L. Nakamura: Routledge.

Hargittai, Eszter. 1999. “Weaving the Western Web: explaining differences in Internet connectivity among OECD countries.” Telecommunications Policy 23:701-718.

Hargittai, Eszter. 2003. “How Wide a Web? Inequalities in Accessing Information Online.” in Sociology Department. Princeton, NJ: Princeton University.

Hargittai, Eszter. 2008. “The Digital Reproduction of Inequality.” Pp. 936-944 in Social Stratification: Class, Race, and Gender in Sociological Perspective, edited by David B. Grusky, Manwai Ku, C., and Szonja Szelényi. Boulder, CO: Westview Press.

Hargittai, Eszter. 2010. “Digital Na(t)ives? Variation in Internet Skills and Uses among Members of the “Net Generation”.” Sociological Inquiry 80:92-113.

Hargittai, Eszter, Lindsay Fullerton, Ericka Menchen-Trevino, and Kristin Yates Thomas. 2010. “Trust Online: Young Adults’ Evaluation of Web Content.” International Journal of Communication 4:468-494.

Hargittai, Eszter, and Amanda Hinnant. 2008. “Digital Inequality: Differences in Young Adults’ Use of the Internet.” Communication Research 35:602-621.

Hargittai, Eszter, and Yu-li Patrick Hsieh. 2010. “Predictors and Consequences of Differentiated Practices on Social Network Sites.” Information, Communication & Society 13(4):515-536.

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Hargittai, Eszter, and Steven Shafer. 2006. “Differences in Actual and Perceived Online Skills: The Role of Gender.” Social Science Quarterly 87:432-448.

Hargittai, Eszter, and Gina Walejko. 2008. “The Participation Divide: Content Creation and Sharing in the Digital Age.” Information, Communication & Society 11:239-256.

Hassani, Sara Nephew. 2006. “Locating Digital Divides at Home, Work, and Everywhere Else.” Poetics 34:250-272.

Haythornthwaite, Caroline, and Ronald E. Rice. 2006. “Perspectives on Internet Use: Access, Involvement and Interaction.” Pp. 92-113 in The Handbook of New Media: Social Shaping and Social Consequences of ICTs, edited by Leah A. Lievrouw and Sonia Livingstone. Thousand Oaks, CA: SAGE Publication.

Heeks, Richard. 2008. “ICT4D 2.0: The Next Phase of Applying ICT for International Development.” Computer 41 (6): 26–33.

Heeks, Richard. 2002. “Information Systems and Developing Countries: Failure, Success, and Local Improvisations.” The Information Society 18 (2): 101–112.

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Horst, Heather A. 2006. The Cell Phone: An Anthropology of Communication. Oxford: Berg.

Howard, Philip E. N., L. E. E. Rainie, and Steve Jones. 2001. “Days and Nights on the Internet: The Impact of a Diffusing Technology.” American Behavioral Scientist 45:383-404.

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Kirschenbaum, Josh, and Radhika Kunamneni. 2001. “Bridging the Organizational Divide: Toward a Comprehensive Approach to the Digital Divide. A PolicyLink Report.” Pp. 34. Oakland, CA: PolicyLink.

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Nemer, D. (2015). From Digital Divide to Digital Inclusion and Beyond. The Journal of Community Informatics, 11(1).

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NTIA. 1999. “Falling Through the Net: Defining the Digital Divide.” Washington DC: US Department of Commerce.

NTIA. 2000. “Falling Through the Net: Toward Digital Inclusion.” Washington DC: US Department of Commerce.

NTIA. 2002. “A Nation Online: Internet Use in America.” Washington DC: US Department of Commerce.

NTIA. 2004. “A Nation Online: Entering the Broadband Age.” Washington DC: US Department of Commerce.

NTIA. 2010. “Digital Nation: 21st Century America’s Progress Towards Universal Broadband Internet Access.” Washington DC: US Department of Commerce.

Nie, Norman, Sunshine Hillygus, and Lutz Erbring. 2002. “Internet Use, Interpersonal Relations and Sociability: A Time Diary Study.” Pp. 244-262 in The Internet in Everyday Life, edited by Barry Wellman and Caroline Haythornthwaite. Oxford: Blackwell.

Norris, Pippa. 2001. Digital Divide? Civic Engagement, Information Poverty & the Internet Worldwide. New York: Cambridge University Press.

Norris, Pippa. 2004. “The Bridging and Bonding Role of Online Communities.” Pp. 31-42 in Society Online: The Interaction in Context, edited by Philip N. Howard and Steve G. Jones. Thousand Oaks, CA: SAGE Publication.

Nussbaum, M. (2000) Women and Human Development: The Capabilities Approach, Cambridge, Cambridge University Press

Oakley, P. (Ed.) (2001) Evaluating Empowerment. Reviewing the Concept and the Practice, Oxford, INTRAC

Ono, Hiroshi, and Madeline Zavodny. 2003. “Gender and the Internet.” Social Science Quarterly 84:111-121.

Ono, Hiroshi, and Madeline Zavodny. 2007. “Digital inequality: A five country comparison using microdata.” Social Science Research 36:1135-1155.

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2015 Advice for Your 856-Year-Old Ph.D.

August 3, 2015

(or, What’s New About Getting an Old Degree?)

I’m delighted to be teaching an intro seminar for all the new Ph.D. students in my department’s graduate program. One of my goals is to give these students a place to talk about the environment of graduate school itself. How does getting a Ph.D. work? What do you need to know?

This task has made me reflective. At first I thought I should pass along readings that had been inspirational for me during grad school. That sure didn’t work. Here is the advice I apparently once loved:

Once you have identified some [thesis] topics you are interested in, you can research them rapidly by spending a few hours on the telephone calling up experts in the field and pumping them for information…although it may cost you a few dollars in long-distance bills…  —Getting What You Came For: The Smart Student’s Guide to Earning a Master’s or Ph.D., p. 182


I wrote the paper with which this book begins on a microcomputer. Though this first experience with one frightened me a little at first, writing soon seemed so much less work that I wondered how I had managed before. —Writing for Social Scientists, p. 151


Having surveyed the basics…it’s time to consider the role that electronic communication can play. The most important thing is to employ electronic media consciously and deliberately as part of a larger strategy for your career. —Networking on the Network: A Guide to Professional Skills for PhD Students


Fortunately, these days every legitimate library has a copy machine, and each copy costs about a dime. —How to Write a Thesis, p. 86

The process of getting a Ph.D. is very old. Wikipedia claims the first Ph.D. was awarded in Paris in 1150. I thought Ph.D. advice would be more likely to stand the test of time.

These days you’ll find better dissertation advice on Tumblr. Or at least you’ll find some comic relief from Tumblrs like When in Academia

when someone asks you how the diss is going

(That’s some great tagging.)

The upshot is that it looks like a fair amount of the advice about how to get a Ph.D. has to do with the available communication technology of the time.  Both the stuff that’s in everyday use, and also the scholarly communication infrastructure (which I’ve also blogged about recently).

Has anyone reading this ever attended a conference paper sale? (No, that’s not about buying pre-written term papers.) Or have you ever received an academic journal article “preprint request postcard?” Here’s an image of one:


Source: Google Scholar Blog.

So far I’ve come up with a list of things that seem to still be helpful. Caveats: I’m aiming to help the social science and humanities students interested in communication and information. Our first year students won’t be teaching yet, so I am not focusing on teaching with this list.

Hopefully there are some readers who will find this list useful too.

How to Get a Ph.D. — The Draft Reading List

Agre, P. (2002). Networking on the Network: A Guide to Professional Skills for PhD Students  I’ll excerpt the following sections:

  • Building a Professional Identity
    • Socializing at Conferences
    • Publication and Credit
    • Recognizing Difference
  • Your Dissertation
  • Academic Language

anonymous. (ed.) (2015). “When in Academia.”

Becker, H. S. (2007). Writing for Social Scientists. Chicago: University of Chicago Press. — Don’t let the title of this book fool you, it is equally applicable to graduate students in the humanities and professional programs. I’m excerpting the following sections:

  • Freshman English for Graduate Students
  • Persona and Authority
  • Learning to Write as a Professional
  • Risk
  • Terrorized by the Literature

Cham, J. (2013, January 21). “Your Conference Presentation.” (image.) PhD Comics.

Edwards, P. N. (2014). “How to Give an Academic Talk.” (13 pp.)

Germano, W. (2013) From Dissertation to Book. (2nd ed.) Chicago: University of Chicago Press. — Note: “Passive Is Spoken Here” is a great section heading. I’ll excerpt the chapter:

  • Making Prose Speak

Sterne, J. (2014). How to Peer Review Something You Hate. ICA Newsletter. (2 pp.)

Shore, B. M. (2014). The Graduate Advisor Handbook. Chicago: University of Chicago Press. I’ll excerpt:

  • Mutual Expectations for Research Advising (pp. 143-146)

Strunk, W., Jr. & White, E. B. (2000). The Elements of Style. (4th ed.) New York: Longman. (Important: You must avoid any “Original Edition” or public domain reprint that does not include E. B. White as a co-author. The version without E. B. White is a different book.)

@yourpapersucks (ed.) (2015). “Shit My Reviewers Say.”


I see that it’s a list woefully lacking in anything like “social media savvy for Ph.D. students” or “How new forms of scholarly communication are changing the dissertation.” I’m sure there are other newish domains I’ve left out, too. What am I missing? Can anyone help me out?  Please add a comment or e-mail me.

Yours in futurity.




(this blog post was cross-posted to Multicast.)


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