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Quantitative Marketing and Economics

Erschienen in:

01.03.2012

Impact of social network structure on content propagation: A study using YouTube data

verfasst von: Hema Yoganarasimhan

Erschienen in: Quantitative Marketing and Economics | Ausgabe 1/2012

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Abstract

We study how the size and structure of the local network around a node affects the aggregate diffusion of products seeded by it. We examine this in the context of YouTube, the popular video-sharing site. We address the endogeneity problems common to this setting by using a rich dataset and a careful estimation methodology. We empirically demonstrate that the size and structure of an author’s local network is a significant driver of the popularity of videos seeded by her, even after controlling for observed and unobserved video characteristics, unobserved author characteristics, and endogenous network formation. Our findings are distinct from those in the peer effects literature, which examines neighborhood effects on individual behavior, since we document the causal relationship between a node’s local network position and the global diffusion of products seeded by it. Our results provide guidelines for identifying seeds that provide the best return on investment, thereby aiding managers conducting buzz marketing campaigns on social media forums. Further, our study sheds light on the other substantive factors that affect video consumption on YouTube.

Vorheriger Artikel Heterogeneity and the dynamics of technology adoption

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In fact, unobserved product quality is problematic in other respects too. A high quality product is more likely to have a higher price, higher consumer ratings, and higher advertising expenditure, i.e., unobserved and observed product attributes are likely to be correlated. Hence, if the former is not controlled for, then our results on the role of observed product attributes are also likely to be biased.

While exists a large stream of literature on Bass models (Bass 1969; Mahajan et al. 2000), these models cannot be used to establish causality between network structure and product diffusion because they assume random mixing or interactions over a fully connected network.

The fact that we are only able to analyze videos whose authors have publicized their friendships may cause some selection bias. There is no direct test to confirm or refute this. However we can test for selection bias indirectly by comparing the viewership distributions of the two samples of authors (i.e., the ones who publicized their friendship links and the ones who didn’t). Since an author’s social network affects her video’s viewership, if we find the viewership distributions of both samples to be similar, then we can infer that both samples are drawn from the same social network distribution. Therefore we compared the viewership distributions of the two samples using Kolmogorov-Smirnov tests at t = 10, 20, and 30. In all three cases, the two distributions were statistically indistinguishable. Hence, we can safely say that any selection bias, if it exists, is not substantial.

Closeness is defined as the average geodesic distance of a node to the rest of the network. In a two-hop network, Closeness is \( c{s_i} = {{{({d_i} + 2s{d_i})}} \left/ {{({d_i} + s{d_i}}} \right.}) \). This can be rewritten as \( c{s_i} = 2 - \left( {{{1} \left/ {{(1 + af{f_i})}} \right.}} \right) \).

Note that there can be more than one geodesic between two nodes. For example, in Fig. 6(b), there are two shortest paths between nodes F3 and F4: F3-S2-F4 and F3-O-F4.

While 2-Beteweenness can be interpreted as a measure of local centrality, many have argued that it is in fact superior to global Betweenness. In 2-Betweenness, only geodesics of length two or less are considered, while global Betweenness considers geodesics of all lengths. However, lengthy paths are seldom used for communication. So taking them into account can result in a distorted picture of centrality. Therefore, some researchers advocate the use of 2-Betweenness even when complete network data is available. See Gould and Fernandez (1989), Friedkin (1991), and Borgatti and Everett (2006) for a comprehensive discussion of these issues. Moreover, both Everett and Borgatti (2005) and Borgatti et al. (2006) have shown that local Betweenness is highly correlated with global Betweenness.

Notice that the degree distribution of first-degree friends looks very different from that of the author’s degree distribution (see Table 3), i.e., Mean (Friend of friends) >Mean (Friends). As noted by Feld (1991), this property is common in social networks because well-connected people (with large Degree) tend to show up disproportionately more often in everyone’s friends lists. Hence, in any social network, random sampling of authors (or nodes) will give rise to a sample of first-degree friends, which will contain some high-degree nodes. In Section 6.4, we perform robustness checks to confirm that our results are not driven by such high-degree nodes.

Stephen and Toubia (2010) also study the impact of local Clustering, but in the context of a sellers’ network. In their setting, a node’s (seller’s) goal is to generate high incoming traffic, whereas in our context a node’s goal is to maximize the outgoing information on video. So their findings are not applicable to our context.

Note that authors from close-knit groups are more likely to post niche videos (videos of interest to only those close to them), which could dampen the global diffusion of their videos. We allow for this possibility within the model through Assumption 10.6, i.e., we allow for correlations between the unobserved content of the videoand its author’s network properties. In the estimation, when specifying moment conditions, we ensure that this correlation is not violated. Hence, we can safely state that the negative effect of Clustering doesn’t stem from the correlation between a video’s content (niche or broad) and the clustering in its author’s network.

On a related note, Granovetter (1973) suggests that new information often comes from weak ties or acquaintances and not from close friends. Since members of close-knit groups tend to be close friends and those of loosely knit groups tend to be acquaintances, this result can also be interpreted as acquaintances being more valuable than close friends from a seed’s perspective.

We expect videos of central authors to receive high attention, thus leading to larger viewership. However, one might suspect otherwise if central authors are also more likely to post many videos in quick succession, thereby diluting the attention per video. To test if this is true, we counted the number of other videos (apart from the one in our dataset) posted by the most central authors. Specifically, of the 1806 authors in our dataset, 116 have a Betweenness value of 1 and these 116 authors posted an average of 0.53 other videos during the interval of our observation. Given our 38-day observation interval, this amounts to a mere 0.0138 videos per day. Thus, the attention fragmentation hypothesis is unlikely to be true.

Both lagged Daily Num. Ratings and lagged Daily Comments are likely to be correlated with lagged Daily Views, which can be problematic. However, we found that normalizing these variables by lagged Daily Views also doesn’t make them significant.

For a sufficiently long T, the number of instruments available for Equations 10 and 11 expands rapidly. While theoretically using all instruments increases consistency, Tauchen (1986) and Ziliak (1997) have shown that there is a consistency-efficiency trade-off in finite samples. In our case, we find that one set of lagged differences and four to six sets of lagged levels are sufficient to get consistent results without any significant loss in efficiency.

Note that we choose 10 periods as the point of demarcation even though ‘early’ in the YouTube context might mean just 4–5 days. We do this primarily because we need sufficient time periods for the analysis. In Model 11, we use 6 lags of Daily views on the right hand side; this leaves us only 4 data points or less per video. If we shortened the span of the early stage, then we would have even fewer data points per video, making analysis difficult.

In the estimation, ln (d _i + 1) is significant in Model 11, but not in Model 12, while ln (sd _i + 1) is significant in Model 12, but not in Model 11 (see Table 8). Given this pattern, the use of a composite variable like ln (aff _i) that contains both sd _i and d _i is problematic because it makes it difficult to ascertain whether d _iis significant in its own right (see Woolridge 2008). So we instead use ln (d _i + 1)and ln (sd _i + 1)directly.

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Titel: Impact of social network structure on content propagation: A study using YouTube data
verfasst von: Hema Yoganarasimhan
Publikationsdatum: 01.03.2012
Verlag: Springer US
Erschienen in: Quantitative Marketing and Economics / Ausgabe 1/2012
Print ISSN: 1570-7156
Elektronische ISSN: 1573-711X
DOI: https://doi.org/10.1007/s11129-011-9105-4