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Similar neural responses predict friendship

Similar neural responses predict friendship

Authors and related work

Overview

A detailed, lay overview has been written up in the New York Times: You Share Everything With Your Bestie. Even Brain Waves.

The study took a cohort (N = 279) of graduate students in a graduate program. Students were asked to list who their friends were, from which a social network was constructed. A subset (N = 42) of these students were then asked to watch a series of videos while their brains were being monitored by an fMRI machine. The timings of brain activations across 80 regions of the brain were compared to see if there were similarities that correlated with social distance. Statistically significant similarities exist such that friends could be identified by firing patterns and timing. Particularly, individuals with one degree of separation were strongly resonant(?), while individuals with three or more degrees of separation could not be discriminated by fMRI.

My more theoretical thoughts:

This is more support for the idea that groups of people “flock” in latent belief space. If everyone fired in the same way to the videos, then the environmental influence would have been dominant – a video of a sloth or a volcano is “objectively” interpreted across a population. Instead, the interpretation of the videos is clustered around individuals with high levels of social connection. Humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3–5, 9–15, 30–45, etc. This is remarkably similar to the numbers found in social organizations such as flocks of starlings (seven). As we’ve seen in multiple studies, a certain amount of social cohesion is beneficial as away of finding resources in a noisy environment (Grunbaum), so this implies that belief space is noisy, but that beneficial beliefs can be found using similar means.  Grunbaum also finds that excessive social cohesion (stampedes) decrease the ability to find resources. Determining the balance of explore/exploit with respect to depending on your neighbors/friends is uncomputable, but exploration is computationally more expensive than exploitation, so the pressure is always towards some level of stampede.

This means that in physical and belief spaces, the density and stiffness of connections controls the behavior of the social network. By adjusting the dial on the similarity aspect (increasing/decreasing stiffness of the links) should result in nomadic, flocking and stampeding behavior in belief space.

Notes

  • Research has borne out this intuition: social ties are forged at a higher-than expected rate between individuals of the same age, gender, ethnicity, and other demographic categories. This assortativity in friendship networks is referred to as homophily and has been demonstrated across diverse contexts and geographic locations, including online social networks [2345(Page 2)
  • When humans do forge ties with individuals who are dissimilar from themselves, these relationships tend to be instrumental, task-oriented (e.g., professional collaborations involving people with complementary skill sets [7]), and short-lived, often dissolving after the individuals involved have achieved their shared goal. Thus, human social networks tend to be overwhelmingly homophilous [8]. (Page 2)
    • This means that groups can be more efficient, but prone to belief stampede
  • Remarkably, social network proximity is as important as genetic relatedness and more important than geographic proximity in predicting the similarity of two individuals’ cooperative behavioral tendencies [4] (Page 2)
  • how individuals interpret and respond to their environment increases the predictability of one another’s thoughts and actions during social interactions [14], since knowledge about oneself is a more valid source of information about similar others than about dissimilar others. (Page 2)
    • There is a second layer on top of this which may be more important. How individuals respond to social cues (which can have significant survival value in a social animal) may be more important than day-to-day reactions to the physical environment.
  • Here we tested the proposition that neural responses to naturalistic audiovisual stimuli are more similar among friends than among individuals who are farther removed from one another in a real-world social network. Measuring neural activity while people view naturalistic stimuli, such as movie clips, offers an unobtrusive window into individuals’ unconstrained thought processes as they unfold [16(page 2)
  • Social network proximity appears to be significantly associated with neural response similarity in brain regions involved in attentional allocation, narrative interpretation, and affective responding (Page 2)
  • We first characterized the social network of an entire cohort of students in a graduate program. All students (N = 279) in the graduate program completed an online survey in which they indicated the individuals in the program with whom they were friends (see Methods for further details). Given that a mutually reported tie is a stronger indicator of the presence of a friendship than an unreciprocated tie, a graph consisting only of reciprocal (i.e., mutually reported) social ties was used to estimate social distances between individuals. (Page 2)
    • I wonder if this changes as people age. Are there gender differences?
  • The videos presented in the fMRI study covered a range of topics and genres (e.g., comedy clips, documentaries, and debates) that were selected so that they would likely be unfamiliar to subjects, effectively constrain subjects’ thoughts and attention to the experiment (to minimize mind wandering), and evoke meaningful variability in responses across subjects (because different subjects attend to different aspects of them, have different emotional reactions to them, or interpret the content differently, for example). (Page 3)
    • I think this might make the influence more environmental than social. It would be interesting to see how a strongly aligned group would deal with a polarizing topic, even something like sports.
  • Mean response time series spanning the course of the entire experiment were extracted from 80 anatomical regions of interest (ROIs) for each of the 42 fMRI study subjects (page 3)
    • 80 possible dimensions. It would be interesting to see this in latent space. That being said, there is no dialog here, so no consensus building, which implies no dimension reduction.
  • To test for a relationship between fMRI response similarity and social distance, a dyad-level regression model was used. Models were specified either as ordered logistic regressions with categorical social distance as the dependent variable or as logistic regression with a binary indicator of reciprocated friendship as the dependent variable. We account for the dependence structure of the dyadic data (i.e., the fact that each fMRI subject is involved in multiple dyads), which would otherwise underestimate the standard errors and increase the risk of type 1 error [20], by clustering simultaneously on both members of each dyad [2122].
  • For the purpose of testing the general hypothesis that social network proximity is associated with more similar neural responses to naturalistic stimuli, our main predictor variable of interest, neural response similarity within each student dyad, was summarized as a single variable. Specifically, for each dyad, a weighted average of normalized neural response similarities was computed, with the contribution of each brain region weighted by its average volume in our sample of fMRI subjects. (Page 3)
  • To account for demographic differences that might impact social network structure, our model also included binary predictor variables indicating whether subjects in each dyad were of the same or different nationalities, ethnicities, and genders, as well as a variable indicating the age difference between members of each dyad. In addition, a binary variable was included indicating whether subjects were the same or different in terms of handedness, given that this may be related to differences in brain functional organization [23]. (page 3)
  • Logistic regressions that combined all non-friends into a single category, regardless of social distance, yielded similar results, such that neural similarity was associated with a dramatically increased likelihood of friendship, even after accounting for similarities in observed demographic variables. More specifically, a one SD increase in overall neural similarity was associated with a 47% increase in the likelihood of friendship(logistic regression: ß = 0.388; SE = 0.109; p = 0.0004; N = 861 dyads)Again, neural similarity improved the model’s predictive power above and beyond observed demographic similarities, χ2(1) = 7.36, p = 0.006. (Page 4)
  • To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again using cluster-robust standard errors to account for dyadic dependencies in the data. This approach is analogous to common fMRI analysis approaches in which regressions are carried out independently at each voxel in the brain, followed by correction for multiple comparisons across voxels. We employed false discovery rate (FDR) correction to correct for multiple comparisons across brain regions. This analysis indicated that neural similarity was associated with social network proximity in regions of the ventral and dorsal striatum … Regression coefficients for each ROI are shown in Fig. 6, and further details for ROIs that met the significance threshold of p < 0.05, FDR-corrected (two tailed) are provided in Table 2. (Page 4)
    • So the latent space that matters involves something on the order of 7 – 9 regions? I wonder if the actions across regions are similar enough to reduce further. I need to look up what each region does.
  • Table 2Figure6
  • Results indicated that average overall (weighted average) neural similarities were significantly higher among distance 1 dyads than dyads belonging to other social distance categories … distance 4 dyads were not significantly different in overall neural response similarity from dyads in the other social distance categories. All reported p-values are two-tailed. (Page 4)
  • Within the training data set for each data fold, a grid search procedure [24] was used to select the C parameter of a linear support vector machine (SVM) learning algorithm that would best separate dyads according to social distance. (Page 5)
  • As shown in Fig. 8, the classifier tended to predict the correct social distances for dyads in all distance categories at rates above the accuracy level that would be expected based on chance alone (i.e., 25% correct), with an overall classification accuracy of 41.25%. Classification accuracies for distance 1, 2, 3, and 4 dyads were 48%, 39%, 31%, and 47% correct, respectively. (Page 6)
  • where the classifier assigned the incorrect social distance label to a dyad, it tended to be only one level of social distance away from the correct answer: when friends were misclassified, they were misclassified most often as distance 2 dyads; when distance 2 dyads were misclassified, they were misclassified most often as distance 1 or 3 dyads, and so on. (Page 6)
  • The results reported here are consistent with neural homophily: people tend to be friends with individuals who see the world in a similar way. (Page 7)
  • Brain areas where response similarity was associated with social network proximity included subcortical areas implicated in motivation, learning, affective processing, and integrating information into memory, such as the nucleus accumbens, amygdala, putamen, and caudate nucleus [27, 28, 29]. Social network proximity was also associated with neural response similarity within areas involved in attentional allocation, such as the right superior parietal cortex [30,31], and regions in the inferior parietal lobe, such as the bilateral supramarginal gyri and left inferior parietal cortex (which includes the angular gyrus in the parcellation scheme used [32]), that have been implicated in bottom-up attentional control, discerning others’ mental states, processing language and the narrative content of stories, and sense-making more generally [3334, 35]. (Page 7)
  • However, the current results suggest that social network proximity may be associated with similarities in how individuals attend to, interpret, and emotionally react to the world around them. (Page 7)
    • Both the environmental and social world
  • A second, not mutually exclusive, possibility pertains to the “three degrees of influence rule” that governs the spread of a wide range of phenomena in human social networks [43]. Data from large-scale observational studies as well as lab-based experiments suggest that wide-ranging phenomena (e.g., obesity, cooperation, smoking, and depression) spread only up to three degrees of geodesic distance in social networks, perhaps due to social influence effects decaying with social distance to the extent that the they are undetectable at social distances exceeding three, or to the relative instability of long chains of social ties [43]. Although we make no claims regarding the causal mechanisms behind our findings, our results show a similar pattern. (Page 8)
    • Does this change with the level of similarity in the group?
  • pre-existing similarities in how individuals tend to perceive, interpret, and respond to their environment can enhance social interactions and increase the probability of developing a friendship via positive affective processes and by increasing the ease and clarity of communication [1415]. (Page 8)

The Radio in Fascist Italy

The Radio in Fascist Italy

  • Philip Cannistraro
  • Journal of European Studies
  • scholars have generally agreed that the control of the mass media by the state is a fundamental prerequisite for the establishment and maintenance of totalitarian dictatorships (pg 127)
  • It is not so widely acknowledged, however, that contemporary totalitarian governments have been largely responsible for the initial growth of the mass media-particularly films and the radio-in their respective countries. (pg 127)
  • In their efforts to expose entire populations to official propaganda, totalitarian regimes encouraged and sponsored the development of the mass media and made them available to every· citizen on a large scale basis. (pg 127)
  • Marconi shrewdly reminded Mussolini that it would be politically wise to place control of the radio in the hands of the state, pointing out the radio’s great potential for propaganda purposes (pg 128)
  • “How many hearts recently beat with emotion when hearing the very voice of the Duce! All this means but one thing: the radio must be extended and extended rapidly. It will contribute much to the general culture of the people” (pg 129)
  • … to insure that EIAR’s programmes conformed to the requirements of the regime’s cultural and political policies. The High Commission included government representatives from each major area of culture: literature, journalism., the fine arts, music, poetry, theatre, and films. The programmes Commission screened the transcripts and plans of all and censored the content of all broadcasts. (pg 130)
  • His broadcast, ‘The Bombardment of Adrianople’, was awaited by the public with great interest and was heralded by critics as the most significant cultural event of the Italian radio.ts Marinetti’s colourful language and emotion-packed presentation blasted un expected life into the Italian radio. His flam.boyant style introduced the concept of the ‘radio personality’ in Fascist Italy, and the success of his talk encouraged those who, like Marinetti himself, hoped to make the radio a new art form. Broadcasts by Marinetti, most of which were lectures on Futurism, continued to be heard on Italian radio each month for more than a decade. (pg 131)
  • The regime quickly recognized the effectiveness of this technique in· arousing listener interest, and it was an easy matter to transfer microphones to mass rallies from where the enthusiastic cheers of the spectators could be heard by radio audiences. (pg 132)
  • The popular announcer Cesare Ferri created the characters ‘Nonno Radio’ (Grandfather Radio) and ‘Zia Radio’ (Aunt Radio), speaking to Italian youth with unprecedented familiarity in terms they easily understood. (pg 132)
  • In order to popular arouse interest in its program.me EIAR sought to stimulate indirect audience participation through public contests for short stories, poems, songs, In and children’s fairy tales. addition, surveys were conducted among listeners to discover trends in popular taste. (pg 133)
  • The radio had an important task to fulfil in the totalitarian state, that of binding the Italians together into one nation through common ideals and a common cultural experience inspired by Fascism. (pg 134)
  • Mussolini proclaimed Radio Rurale a great achievement of the Fascist revolution, for contemporary observers saw it as a new instrument with which to integrate rural existence into the mainstream. of national life. (pg 135)
  • The measures taken by the regime to overcome cultural and political provincialism by creating a mass radio audience in the countryside met with qualified success. (pg 137)
  • Regarded by many as an important step towards the creation of a truly popular culture, Radio Btdilla’s purpose was to give the working classes of the city and the countryside the means of acquiring a radio at a modest cost. Through the radio art, instruction, music, poetry-all the cultural masterworks–cease to become the privilege and unjust monopoly of a few elitist groups’. (pg 139)
  • ‘The ministry, in carrying out its delicate functions of vigilance over radio broadcasting, must guide itself by criteria that are essentially of a political and cultural nature.’ (pg 140)
  • Once the radio had been integrated into the structure of the Ministry of Popular Culture, the Fascists began to develop m.ore effective ways of using broadcasting as a cultural medium. While the number and variety of programmes had begun to increase by the beginning of the decade, it was only after 1934 that they became politically sophisticated. (pg 141)
  • Fascist racial doctrines became a major theme of radio propaganda during World War II. An Italo-German accord signed in 1940 to co-ordinate radio propaganda between the two countries included measures to ‘intensify anti-Jewish propaganda’ on the Italian radio as well as in foreign broadcasts.78 The Inspectorate for Radio Broadcasting organized an important series of anti-Semitic prograrnm.es that centred around the ‘Protocols of Zion’, and talks such as ‘Judaism. versus Western Culture’, the ‘Jewish International’, and ‘Judaism. Wanted this War’, were broadcast from 1941 to 1943. (pg 143)
  • information received from the Vatican radio during World War II was generally regarded more accurate than the obvious propaganda broadcasts of the Allies (pg 147)
  • On the radio he astutely employed direct, forceful language, shouting short and vivid sentences to create a sense of drama and arouse emotional reactions. ‘This ‘maniera forte’ that characterized Appelius’ radio talks had a great appeal for many Italians, especially for the ‘little man’ who wanted to be talked to on his own level in terms he could readily understand.121 In his broadcasts Appelius screamed insults and ranted and raved at the foul enemies of Fascism. with a powerful barrage of verbal abuse, inciting his audiences to unmitigated hatred and scorn against the evil ‘anglo-sassoni’ and their allies. (pg 150)
  • In the broad context of Fascist cultural aspirations, all the media aimed at similar goals: the diffusion of standard images and themes that reflected the ideological values of Fascism.; the creation of a mass culture that conformed the needs of the Fascist state in its capacity as a totalitarian to government. (pg 154)

Sick echo chambers

Over the past year, I’ve been building a model that lets me look at how opinions evolve in belief space, much in the manner that flocks, herds and schools emerge in the wild.

CI_GP_Poster4a

Recently, I was Listening to BBC Business Daily this morning on Facebook vs Democracy:

  • Presenter Ed Butler hears a range of voices raising concern about the existential threat that social media could pose to democracy, including Ukrainian government official Dmytro Shymkiv, journalist Berit Anderson, tech investor Roger McNamee and internet pioneer Larry Smarr.

Roger McNamee and Larry Smarr in particular note how social media can be used to increase polarization based on emergent poles. In other words, “normal” opposing views can be amplified by attentive bad actors [page 24] with an eye towards causing generalized societal disruption.

My model explores emergent group interactions and I wondered if this adversarial herding in information space as it might work in my model.

These are the rough rules I started with:

  • Herders can teleport, since they are not emotionally invested in their belief space position and orientation
  • Herders appear like multiple individuals that may seem close and trustworthy, but they are actually a distant monolithic entity that is aware of a much larger belief space.
  • Herders amplify arbitrary pre-existing positions. The insight is that they are not herding in a direction, but to increase polarization
  • To add this to the model, I needed to do the following:
    • Make the size of the agent a function of the weight so we can see what’s going on
    • When in ‘herding mode’ the overall heading of the population is calculated, and the agent that is closest to that heading is selected to be amplified by our trolls/bot army.
    • The weight is increased to X, and the radius is increased to Y.
      • X represents AMPLIFICATION BY trolls, bots, etc.
      • A large Y means that the bots can swamp other, normally closer signals. This models the effect of a monolithic entity controlling thousands of bots across the belief space

Here’s a screenshot of the running simulation. There is an additional set of controls at the upper left that allow herding to be enables, and the weight of the influence to be set. In this case, the herding weight is 10. Though the screenshot shows one large agent shape, the amplified shape flits from agent to agent, always keeping closest to the average heading.

2017-10-28

The results are kind of scary. If I set the weight of the herder to 15, I can change the change the flocking behavior of the default to echo chamber.

  • Normal: No Herding
  • Herding weight set to 15, other options the same: HerdingWeight15

I did some additional tweaking to see if having highly-weighted herders ignore each other (they would be coordinated through C&C) would have any effect. It doesn’t. There is enough interaction through the regular populations to keep the alignment space reduced.

It looks like there is a ‘sick echo chamber’ pattern. If the borders are reflective, and the herding weight + influence radius is great enough, then a wall-hugging pattern will emerge.

The influence weight is sort of a credibility score. An agent that has a lot of followers, or says a lot of the things that I agree with has a lot of influence weight The range weight is reach.

Since a troll farm or botnet can be regarded as a single organization,  interacting with any one of the agents is really interacting with the root entity.  So a herding agent has high influence and high reach. The high reach explains the border hugging behavior.

It’s like there’s someone at the back of the stampede yelling YOUR’E GOING THE RIGHT WAY! KEEP AT IT! And they never go off the cliff because they are a swarm Or, it never goes of the cliff, because it manifests as a swarm.

A loud, distributed voice pointing in a bad direction means wall hugging. Note that there is some kind of floating point error that lets wall huggers creep off the edge.Edgecrawling

With a respawn border, we get the situation where the overall heading of the flock doesn’t change even as it gets destroyed as it goes over the border. Again, since the herding algorithm is looking at the overall population, it never crosses the border but influences all the respawned agents to head towards the same edge: DirectionPreserving

Who’d have thought that there could be something worse than runaway polarization?

The Gamergate model of press relations

This is good enough that I’m reposting the entire thing. The original is here.

PRESSTHINK, a project of the Arthur L. Carter Journalism Institute at New York University, is written by Jay Rosen

I remember the first time I heard about Gamergate. A random follower on Twitter asked me if I had been following the story, which he said was “about ethics in games journalism.” No, I had not been following the story. In all innocence, I clicked on the link he sent me and tried to make sense of what I read. I failed. The events it described were impenetrable to me. (Disclosure: I am not a gamer.)

Eventually I learned what Gamergate really was. The more I learned, the more depressed I felt. The people who promoted Gamergate said they were concerned about journalism ethics. As a professor of journalism with a social media bent, I felt obligated to examine their claims. When I did I discovered nasty troll behavior with a hard edge of misogyny. “It’s about ethics in games journalism” became an internet joke. Deservedly so.

Recently Ben Smith, the editor-in-chief of Buzzfeed’s news operation, wrote: “The big story of 2014 was Gamergate, the misogynistic movement championed by Breitbart and covered primarily by new media. That turned out to be a better predictor of the presidential election than any rubber chicken dinner in Iowa (or poll by a once-reputable pollster).”

Ben is right. The Gamergate model in press relations posits that high-risk tactics should not be ruled out of consideration. It says that rejection and ridicule by the mainstream media can be a massive plus, because events like these activate — and motivate  — your most committed supporters: your trolls. The Gamergate model proposes that transgressing the norms of American democracy is not some crippling defect, as previously believed, but a distinct advantage because the excitement around the transgression recruits new players to the fight, and guarantees the spread of your content.

The Gamergate model anticipates that the mainstream press will freak out. Full stop. And it seeks to profit from this reaction. What the traditional press considers negative publicity is, from the Gamergate point of view, a kind of gift to The Leader. Trump and his advisors have absorbed these lessons. Gamergate is thus one possible template for the future of White House-press corps relations. Those who have not studied it carefully will be at a distinct disadvantage.

Filter bubbles, echo chambers, and online news consumption

Filter bubbles, echo chambers, and online news consumption

  • Seth R. Flaxman – I am currently undertaking a postdoc with Yee Whye Teh at Oxford in the computational statistics and machine learning group in the Department of Statistics. My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science areas including crime, emotion, and public health. I helped make a very accessible animation answering the question, What is Machine Learning?
  • Sharad Goel – I’m an Assistant Professor at Stanford in the Department of Management Science & Engineering (in the School of Engineering). I also have courtesy appointments in Sociology and Computer Science. My primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. I’m particularly interested in applying modern computational and statistical techniques to understand and improve public policy.
  • Justin M. Rao – I am a Senior Researcher at Microsoft Research. A member of our New York City lab, an interdisciplinary research group combining social science with computational and theoretical methods, I am currently located at company HQ in the Seattle area, where I am also an Affiliate Professor of Economics at the University of Washington.
  • Spearman’s Rank-Order Correlation
  • Goel, Mason, and Watts (2010) show that a substantial fraction of ties in online social networks are between individuals on opposite sides of the political spectrum, opening up the possibility for diverse content discovery. [p 299]
    • I think this helps in areas where flocking can occur. Changing heading is hardest when opinions are moving in opposite directions. Finding a variety of perspectives may change the dynamic.
  • Specifically, users who predominately visit left-leaning news outlets only very
    rarely read substantive news articles from conservative sites, and vice versa
    for right-leaning readers, an effect that is even more pronounced for opinion
    articles.

    • Is the range of information available from left or right-leaning sites different? Is there another way to look at the populations? I think it’s very easy to get polarized left or right, but seeking diversity is different, and may have a pattern of seeking less polarized voices?
  • Interestingly, exposure to opposing perspectives is higher for the
    channels associated with the highest segregation, search, and social. Thus,
    counterintuitively, we find evidence that recent technological changes both
    increase and decrease various aspects of the partisan divide.

    • To me this follows, because anti belief helps in the polarization process.
  • We select an initial universe of news outlets (i.e., web domains) via the Open Directory Project (ODP, dmoz.org), a collective of tens of thousands of editors who hand-label websites into a classification hierarchy. This gives 7,923 distinct domains labeled as news, politics/news, politics/media, and regional/news. Since the vast majority of these news sites receive relatively little traffic,
    •  Still a good option for mapping. Though I’d like to compare with schema.org
  • Specifically, our primary analysis is based on the subset of users who have read at least ten substantive news articles and at least two opinion pieces in the three-month time frame we consider. This first requirement reduces our initial sample of 1.2 million individuals to 173,450 (14 percent of the total); the second requirement further reduces the sample to 50,383 (4 percent of the total). These numbers are generally lower than past estimates, likely because of our focus on substantive news and opinion (which excludes sports, entertainment, and other soft news), and our explicit activity measures (as opposed to self-reports).
    • Good indicator of explore-exploit in the user population at least in the context of news.
  • We now define the polarity of an individual to be the typical polarity of the news outlet that he or she visits. We then define segregation to be the expected distance between the polarity scores of two randomly selected users. This definition of segregation, which is in line with past work (Dandekar, Goel, and Lee 2013), intuitively captures the idea that segregated populations are those in which pairs of individuals are, on average, far apart.
    • This fits nicely with my notion of belief space
  • ideological-segregation-across-channels
    • This is interesting. Figure 3 shows that aggregators and direct (which have some level of external curation, are substantially less polarized than the social and search-based channels. That’s a good indicator that the visible information horizon makes a difference in what is accessed.
  • our findings do suggest that the relatively recent ability to instantly query large corpora of news articles—vastly expanding users’ choice sets—contributes to increased ideological segregation
    • The frictionlessness of being able to find exactly what you want to see, without being exposed to things that you disagree with.
  • In particular, that level of segregation corresponds to the ideological distance between Fox News and Daily Kos, which represents meaningful differences in coverage (Baum and Groeling 2008) but is within the mainstream political spectrum. Consequently, though the predicted filter bubble and echo chamber mechanisms do appear to increase online segregation, their overall effects at this time are somewhat limited.
    • But this depends on how opinion is moving. We are always redefining normal. It would also be good to look at the news producers using this approach…?
  • This finding of within-user ideological concentration is driven in part by the fact that individuals often simply turn to a single news source for information: 78 percent of users get the majority of their news from a single publication, and 94 percent get a majority from at most two sources. …even when individuals visit a variety of news outlets, they are, by and large, frequenting publications with similar ideological perspectives.
  • opposingpartisanexposure
    • Although I think focussing on ‘opposing’ rather than ‘diverse’ biases these results, this still shows that populations of users behave differently, and that the channel has a distinct effect.
  • …relatively high within-user variation is a product of reading a variety of centrist and right-leaning outlets, and not exposure to truly ideologically diverse content.
    • So left leaning is more diverse across ideology
  • the outlets that dominate partisan news coverage are still relatively mainstream, ranging from the New York Times on the left to Fox News on the right; the more extreme ideological sites (e.g., Breitbart), which presumably benefited from the rise of online publishing, do not appear to qualitatively impact the dynamics of news consumption.

Some good parts, kind of integrated

We are packing up, so I’m done for now. Progress has been pretty good. The core parts of the posting module are done, though they are not yet managed by a “topic manager” or some similar. I have YUI talking via PHP to MySQL, sending objects that contain data that will be needed in a structured way. That turned out to be much harder than I thought, simply because I couldn’t make the debugger in IntelleJ work in the PHP server file in such a way that I could watch an HTTP request come in. In olden days, I would have RTFM about the process and worked from that, but now with OpenSource, I’ve become very dependent on the debugger to tell me what’s actually going on. Many times things don’t correspond with (often stale) documentation. So in the end, I put together a light PHP class that pretty much echoed POST calls back at me so that I could look at them in the JavaScript debugger. That burned a day. Sigh.

The last (new) thing to do is to make the database access robust. I did my code based on Learning PHP, MySQL, and JavaScript, a generally fine book, but it still uses the deprecated “mysql_*” calls. I need to update that and have some generalized data return structures built. Then that part should be reasonably static from here on out.