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00:00:12.760 --> 00:00:29.536 What keeps us healthy and happy as we go through life? If you were going to invest now in your future best self, where would you put your time and your energy? There was a recent survey of millennials http://youtu.be/8KkKuTCFvzI?t=12

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00:00:29.560 --> 00:00:49.720 asking them what their most important life goals were, and over 80 percent said that a major life goal for them was to get rich. And another 50 percent of those same young adults said that another major life goal was to become famous. http://youtu.be/8KkKuTCFvzI?t=29

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00:00:50.960 --> 00:01:08.696 (Laughter) And we're constantly told to lean in to work, to push harder and achieve more. We're given the impression that these are the things that we need to go after in order to have a good life. Pictures of entire lives, http://youtu.be/8KkKuTCFvzI?t=50

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00:01:08.720 --> 00:01:33.136 of the choices that people make and how those choices work out for them, those pictures are almost impossible to get. Most of what we know about human life we know from asking people to remember the past, and as we know, hindsight is anything but 20/20. We forget vast amounts of what happens to us in life, http://youtu.be/8KkKuTCFvzI?t=68

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00:01:33.160 --> 00:01:54.200 and sometimes memory is downright creative. But what if we could watch entire lives as they unfold through time? What if we could study people from the time that they were teenagers all the way into old age to see what really keeps people happy and healthy? http://youtu.be/8KkKuTCFvzI?t=93

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00:01:55.560 --> 00:02:22.256 We did that. The Harvard Study of Adult Developmen 0 may be the longest study of adult life that's ever been done. For 75 years, we've tracked the lives of 724 men, year after year, asking about their work, their home lives, their health, and of course asking all along the way without knowing how their life stories http://youtu.be/8KkKuTCFvzI?t=115

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00:02:22.280 --> 00:02:41.376 were going to turn out. Studies like this are exceedingly rare. Almost all projects of this kind fall apart within a decade because too many people drop out of the study, or funding for the research dries up, or the researchers get distracted, http://youtu.be/8KkKuTCFvzI?t=142

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00:02:41.400 --> 00:03:00.336 or they die, and nobody moves the ball further down the field. But through a combination of luck and the persistence of several generations of researchers, this study has survived. About 60 of our original 724 men are still alive, http://youtu.be/8KkKuTCFvzI?t=161

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00:03:00.360 --> 00:03:20.136 still participating in the study, most of them in their 90s. And we are now beginning to study the more than 2,000 children of these men. And I'm the fourth director of the study. Since 1938, we've tracked the lives of two groups of men. http://youtu.be/8KkKuTCFvzI?t=180

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00:03:20.160 --> 00:03:37.616 The first group started in the study when they were sophomores at Harvard College. They all finished college during World War II, and then most went off to serve in the war. And the second group that we've followed was a group of boys from Boston's poorest neighborhoods, http://youtu.be/8KkKuTCFvzI?t=200

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00:03:37.640 --> 00:03:56.416 boys who were chosen for the study specifically because they were from some of the most troubled and disadvantaged families in the Boston of the 1930s. Most lived in tenements, many without hot and cold running water. When they entered the study, http://youtu.be/8KkKuTCFvzI?t=217

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00:03:56.440 --> 00:04:16.136 all of these teenagers were interviewed. They were given medical exams. We went to their homes and we interviewed their parents. And then these teenagers grew up into adults who entered all walks of life. They became factory workers and lawyers and bricklayers and doctors, http://youtu.be/8KkKuTCFvzI?t=236

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00:04:16.160 --> 00:04:38.456 one President of the United States. Some developed alcoholism. A few developed schizophrenia. Some climbed the social ladder from the bottom all the way to the very top, and some made that journey in the opposite direction. The founders of this study http://youtu.be/8KkKuTCFvzI?t=256

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00:04:38.480 --> 00:04:59.120 would never in their wildest dreams have imagined that I would be standing here today, 75 years later, telling you that the study still continues. Every two years, our patient and dedicated research staff calls up our men and asks them if we can send them yet one more set of questions about their lives. http://youtu.be/8KkKuTCFvzI?t=278

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00:05:00.040 --> 00:05:26.736 Many of the inner city Boston men ask us, "Why do you keep wanting to study me? My life just isn't that interesting." The Harvard men never ask that question. (Laughter) To get the clearest picture of these lives, we don't just send them questionnaires. http://youtu.be/8KkKuTCFvzI?t=300

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00:05:26.760 --> 00:05:45.256 We interview them in their living rooms. We get their medical records from their doctors. We draw their blood, we scan their brains, we talk to their children. We videotape them talking with their wives about their deepest concerns. And when, about a decade ago, we finally asked the wives http://youtu.be/8KkKuTCFvzI?t=326

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00:05:45.280 --> 00:06:01.496 if they would join us as members of the study, many of the women said, "You know, it's about time." (Laughter) So what have we learned? What are the lessons that come from the tens of thousands of pages of information that we've generated http://youtu.be/8KkKuTCFvzI?t=345

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00:06:01.520 --> 00:06:30.936 on these lives? Well, the lessons aren't about wealth or fame or working harder and harder. The clearest message that we get from this 75-year study is this: Good relationships keep us happier and healthier. Period. We've learned three big lessons about relationships. The first is that social connections are really good for us, http://youtu.be/8KkKuTCFvzI?t=361

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00:06:30.960 --> 00:06:51.816 and that loneliness kills. It turns out that people who are more socially connected to family, to friends, to community, are happier, they're physically healthier, and they live longer than people who are less well connected. And the experience of loneliness turns out to be toxic. http://youtu.be/8KkKuTCFvzI?t=390

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00:06:51.840 --> 00:07:13.256 People who are more isolated than they want to be from others find that they are less happy, their health declines earlier in midlife, their brain functioning declines sooner and they live shorter lives than people who are not lonely. And the sad fact is that at any given time, http://youtu.be/8KkKuTCFvzI?t=411

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00:07:13.280 --> 00:07:33.176 more than one in five Americans will report that they're lonely. And we know that you can be lonely in a crowd and you can be lonely in a marriage, so the second big lesson that we learned is that it's not just the number of friends you have, and it's not whether or not you're in a committed relationship, http://youtu.be/8KkKuTCFvzI?t=433

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00:07:33.200 --> 00:08:01.056 but it's the quality of your close relationships that matters. It turns out that living in the midst of conflict is really bad for our health. High-conflict marriages, for example, without much affection, turn out to be very bad for our health, perhaps worse than getting divorced. And living in the midst of good, warm relationships is protective. Once we had followed our men all the way into their 80s, http://youtu.be/8KkKuTCFvzI?t=453

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00:08:01.080 --> 00:08:17.280 we wanted to look back at them at midlife and to see if we could predic 6 who was going to grow into a happy, healthy octogenarian and who wasn't. And when we gathered together everything we knew about them at age 50, http://youtu.be/8KkKuTCFvzI?t=481

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00:08:18.080 --> 00:08:38.856 it wasn't their middle age cholesterol levels that predicted how they were going to grow old. It was how satisfied they were in their relationships. The people who were the most satisfied in their relationships at age 50 were the healthiest at age 80. And good, close relationships seem to buffer us http://youtu.be/8KkKuTCFvzI?t=498

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00:08:38.880 --> 00:08:57.656 from some of the slings and arrows of getting old. Our most happily partnered men and women reported, in their 80s, that on the days when they had more physical pain, their mood stayed just as happy. But the people who were in unhappy relationships, http://youtu.be/8KkKuTCFvzI?t=518

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00:08:57.680 --> 00:09:19.096 on the days when they reported more physical pain, it was magnified by more emotional pain. And the third big lesson that we learned about relationships and our health is that good relationships don't just protect our bodies, they protect our brains. It turns out that being in a securely attached relationship http://youtu.be/8KkKuTCFvzI?t=537

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00:09:19.120 --> 00:09:37.576 to another person in your 80s is protective, that the people who are in relationships where they really feel they can count on the other person in times of need, those people's memories stay sharper longer. And the people in relationships where they feel they really can't count on the other one, http://youtu.be/8KkKuTCFvzI?t=559

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00:09:37.600 --> 00:09:56.376 those are the people who experience earlier memory decline. And those good relationships, they don't have to be smooth all the time. Some of our octogenarian couples could bicker with each other day in and day out, but as long as they felt that they could really count on the other when the going got tough, http://youtu.be/8KkKuTCFvzI?t=577

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00:09:56.400 --> 00:10:19.016 those arguments didn't take a toll on their memories. So this message, that good, close relationships are good for our health and well-being, this is wisdom that's as old as the hills. Why is this so hard to get and so easy to ignore? Well, we're human. http://youtu.be/8KkKuTCFvzI?t=596

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00:10:19.040 --> 00:10:37.176 What we'd really like is a quick fix, something we can ge 0 that'll make our lives good and keep them that way. Relationships are messy and they're complicated and the hard work of tending to family and friends, it's not sexy or glamorous. http://youtu.be/8KkKuTCFvzI?t=619

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00:10:37.200 --> 00:11:02.136 It's also lifelong. It never ends. The people in our 75-year study who were the happiest in retiremen 6 were the people who had actively worked to replace workmates with new playmates. Just like the millennials in that recent survey, many of our men when they were starting out as young adults really believed that fame and wealth and high achievemen http://youtu.be/8KkKuTCFvzI?t=637

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t00:11:02.16 --> 00:11:26.840 6 were what they needed to go after to have a good life. But over and over, over these 75 years, our study has shown that the people who fared the best were the people who leaned in to relationships, with family, with friends, with community. So what about you? Let's say you're 25, or you're 40, or you're 60. http://youtu.be/8KkKuTCFvzI?t=NaN

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00:11:27.800 --> 00:11:54.216 What might leaning in to relationships even look like? Well, the possibilities are practically endless. It might be something as simple as replacing screen time with people time or livening up a stale relationship by doing something new together, long walks or date nights, or reaching out to that family member who you haven't spoken to in years, http://youtu.be/8KkKuTCFvzI?t=687

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00:11:54.240 --> 00:12:14.296 because those all-too-common family feuds take a terrible toll on the people who hold the grudges. I'd like to close with a quote from Mark Twain. More than a century ago, he was looking back on his life, http://youtu.be/8KkKuTCFvzI?t=714

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00:12:14.320 --> 00:12:39.136 and he wrote this: "There isn't time, so brief is life, for bickerings, apologies, heartburnings, callings to account. There is only time for loving, and but an instant, so to speak, for that." The good life is built with good relationships. http://youtu.be/8KkKuTCFvzI?t=734

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00:12:39.160 --> 00:12:45.840 Thank you. (Applause) http://youtu.be/8KkKuTCFvzI?t=759

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      network structure:
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      Network Structure Insights
       
      mind-viral immunity:
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      stucture:
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        ?
      The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

      In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
      The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
      The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

      Modularity
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      Topics Nodes in Top Topic Components Nodes in Top Comp
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      Narrative Influence Propagation:
        ?
      The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

      The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

      The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
      propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
      We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

      Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

      For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

      Degree Distribution:
        calculate & show   ?
      (based on kolmogorov-smirnov test) ?   switch to linear
      Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

      This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
      If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

      Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
      ×  ⁝⁝ 
                 
      Main Topical Groups:

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      +     full stats   ?  

      The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

      We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
      Most Influential Elements:
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      We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

      Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

      The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
      Network Structure:
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      Structural Gap
      (ask a research question that would link these two topics):
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      Reveal the Gap   Generate a Question   ?
       
      A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

      This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

      Latent Topical Brokers
      :
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      These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

      These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

      Emerging Keywords
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      Evolution of Topics
      (frequency / time) ?
      The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.

      Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

       
      Main Topics
      (according to Latent Dirichlet Allocation):
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      LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

      We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
      Most Influential Words
      (main topics and words according to LDA):
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      We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

      Sentiment Analysis

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      We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

      The approach is based on AFINN and Emoji Sentiment Ranking

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
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      Top Relations / Bigrams
      (both directions):

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      The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

      As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
      Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

       
      We will build two graphs:
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      Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

       
      We will build two graphs:
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      You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
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