Network Analysis of Recurring YouTube Spam Campaigns

Network Analysis of Recurring YouTube Spam Campaigns


As the popularity of content sharing websites such as YouTube and Flickr has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to bogus e-commerce websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies. One of these strategies uses a small number of spam user accounts to comment on a large number of videos, whereas a larger number of accounts is used with the other. We present an evaluation that uses motif profiling to track two active campaigns matching these strategies, and identify some of the associated user accounts.


1 Introduction

The usage and popularity of content sharing websites continues to rise each year. For example, the number of Flickr uploads has risen to a total of six billion images, having increased annually by 20% over the past five years1. Similarly, YouTube now receives more than three billion views per day, with forty-eight hours of video being uploaded every minute; increases of 50% and 100% respectively over the previous year2. Unfortunately, such increases have also resulted in these sites becoming more lucrative targets for spammers hoping to attract unsuspecting users to malicious websites, where a variety of threats such as scams (phishing, e-commerce) and malware can be found. This is a particular problem for YouTube given its facility to host discussions in the form of video comments [\citeauthoryearSureka2011]. Opportunities exist for the abuse of this feature with the availability of bots3 that can be used to post spam comments in large volumes.


Figure 1: Strategies of two spam campaigns targeting YouTube in 2011 - small number of accounts each commenting on many videos (left), and larger number of accounts each commenting on few videos (right). Blue nodes are videos, red nodes are accounts marked as spam, beige nodes are spam accounts not marked accordingly.

Our investigation has found that bot-posted spam comments are often associated with orchestrated campaigns that can remain active for long periods of time, where the primary targets are popular videos. Such campaigns tend to employ a variety of detection evasion techniques, such as variants of the same fundamental message content, perhaps with different website domains, and an ever-evolving set of fake user accounts. An initial manual analysis of data gathered from YouTube revealed activity from a number of campaigns, two of which can be seen in Figure 1. The results presented in this paper confirm the presence of these campaigns, along with their recurring nature.

As an alternative to traditional approaches that attempt to detect spam on an individual (comment) level (e.g. domain blacklists), this paper presents an evaluation of the detection of these recurring campaigns using network analysis, based on networks derived from the comments posted by users to videos. This approach uses the concept of network motif profiling [\citeauthoryearMilo et al.2002, \citeauthoryearMilo et al.2004, \citeauthoryearWu, Harrigan, and Cunningham2011], where motif counts from the derived networks are tracked over time. Given that different campaign strategies can exist (see Figure 1), the objective is to discover certain discriminating motifs that can be used to identify particular strategies and the associated users as they periodically recur.

This paper begins with a description of related work in the domain. The collection of contemporary YouTube data, comprised of comments posted to the most popular videos over a period of time, is then discussed. Next, the methodology used by the detection approach is described in detail, from derivation of the comment-based networks to the subsequent network motif profile generation. The results of an experiment for a seventy-two hour period are then presented. These results demonstrate the use of certain discriminating motifs to identify some of the users associated with two separate campaigns we have discovered within this time period. Further analysis of the campaign websites is also provided. Finally, the overall conclusions are discussed, and some suggestions for future work are made.

2 Related Work

2.1 Structural and spam analysis

The network structure of YouTube has been analysed in a number of separate studies. Paolillo et al.  \shortcitePaolillo08 investigated the social structure with the generation of a user network based on the friendship relationship, focusing on the degree distribution. They found that YouTube is similar to other online social networks with respect to degree distribution, and that a social core exists between authors (uploaders) of videos. An alternative network based on related videos was analysed by Cheng et al.  \shortciteCheng08:4539688. Given that the resulting networks were not strongly connected, attention was reserved for the largest strongly connected components. These components were found to exhibit small-world characteristics [\citeauthoryearWatts and Strogatz1998], with large clustering coefficients and short characteristic path lengths, indicating the presence of dense clusters of related videos.

Benevenuto et al.  \shortciteBenevenuto:2008:UVI:1459359.1459480 created a directed network based on videos and their associated responses. Similarly, they found that using the largest strongly connected components was more desirable due to the large clustering coefficients involved. This was a precursor to subsequent work concerned with the detection of spammers and content promoters within YouTube [\citeauthoryearBenevenuto et al.2008b, \citeauthoryearBenevenuto et al.2009]. Features from the video responses networks (e.g. clustering coefficient, reciprocity) were used as part of a larger set to classify users accordingly. Other YouTube spam investigations include the recent work of Sureka \shortciteDBLP:journals/corr/abs-1103-5044, based on the detection of spam within comments posted to videos. A number of features were derived to analyse the overall activity of users, rather than focusing on individual comment detection.

An extensive body of work has been dedicated to the analysis of spam within other online social networking sites. For example, Mishne et al.  \shortciteMishne05blockingblog suggested an approach for the detection of link spam within blog comments using the comparison of language models. Gao et al.  \shortciteGao:2010:DCS:1879141.1879147 investigated the proliferation of spam within Facebook “wall” messages, with the detection of spam clusters using networks based on message similarity. This particular study demonstrated the bursty (recurring) and distributed aspects of botnet-driven spam campaigns, as discussed by Xie et al.  \shortciteXie:2008:SBS:1402958.1402979. The shortcomings of URL blacklists for the prevention of spam on Twitter were highighted by Grier et al.  \shortciteGrier:2010:SUC:1866307.1866311, where it was found that blacklist update delays of up to twenty days can occur. This is a particular problem with the use of shortened URLs, the nature of which was recently analysed by Chhabra et al.  \shortciteChhabra:2011:PPL:2030376.2030387.

2.2 Network motif analysis

Network motifs [\citeauthoryearMilo et al.2002, \citeauthoryearShen-Orr et al.2002] are structural patterns in the form of interconnected -node subgraphs that are considered to be inherent in many varieties of network, such as biological, technological and sociological networks. They are often used for the comparison of said networks, and can also indicate certain network characteristics. In particular, the work of Milo et al.  \shortciteMilo05032004 proposed the use of significance profiles based on the motif counts found within networks to enable the comparison of local structure between networks of different sizes. In this case, the generation of an ensemble of random networks was required for each significance profile. An alternative to this approach [\citeauthoryearWu, Harrigan, and Cunningham2011] involved the use of motif profiles that did not entail random network generation. Instead, profiles were created on an egocentric basis for the purpose of characterising individual egos, encompassing the motif counts from the entirety of egocentric networks within a particular network.

The domain of spam detection has also profited from the use of network motifs or subgraphs. Within a network built from email addresses [\citeauthoryearBoykin and Roychowdhury2005], a low clustering coefficient (based on the number of triangle structures within a network) may indicate the presence of spam addresses, with regular addresses generally forming close-knit communities, i.e. a relatively higher number of triangles. Becchetti et al.  \shortciteBecchetti:2008:ESA:1401890.1401898 made use of the number of triangles and clustering coefficient as features in the detection of web spam. These two features were found to rank highly within an overall feature set. Motifs of size three (triads) have also been used to detect spam comments in networks generated from blog interaction [\citeauthoryearKamaliha et al.2008]. It was found that certain motifs were likely to indicate the presence of spam, based on comparison with corresponding random network ensembles.

Separately, network motifs have also been used to characterize network traffic [\citeauthoryearAllan, Turkett, and Fulp2009]. A network was created for each application (e.g. HTTP, P2P applications), and nodes within the network were classified using corresponding motif profiles.

3 Data Collection

Following the lead of earlier related YouTube research, a data set was collected in order to permit the investigation of contemporary spam comment activity. An extensive crawl of the YouTube network was performed by other researchers [\citeauthoryearPaolillo2008, \citeauthoryearBenevenuto et al.2009]. In our case, we opted for a specific selection of the available data given that spam comments in YouTube tend to be directed towards a subset of the entire video set, i.e. more popular videos generally have a higher probability of attracting attention from spammers, thus ensuring a larger audience. This characteristic has also been seen on other online social networks such as Twitter [\citeauthoryearBenevenuto et al.2010].

Another issue to be considered is the accessibility of certain YouTube data attributes. The recent activity of a user profile contains a number of potential attributes for use in the derivation of representative networks, such as comments posted to videos, and subscriptions added to other users. Similarly, the list of subscribers for a particular user would also be useful. However, access to these attributes can often be restricted, meaning that reliance on such data may lead to inaccuracies during subsequent experiments. On the contrary, comments (and the users who posted them) found on a public video’s page are always accessible. Given these issues, we decided to use only data to which access was not restricted, namely the comments posted to videos along with the associated user accounts.

3.1 Retrieval process

The data has been retrieved using the YouTube Data API4. This API provides access to video and user profile information. There are some limits associated with using the API, of which further details are provided below. Apart from video and user information, access is also provided to standard feeds such as Most Viewed videos, Top Rated videos etc. The fact that these feeds are periodically updated (usually daily) facilitates our objective of analysing recurring spam campaigns, as it enables the retrieval of popular videos (i.e. those attracting spam comments) on a continual basis. Therefore, the retrieval process is executed periodically as follows:

  1. Retrieve the current video list from the most viewed standard feed for the US region (the API limits this to a maximum of 100 videos).

  2. For each video in the list:

    1. If this video has not appeared in an earlier feed list, retrieve its meta-data such as upload time, description etc.

    2. Retrieve the comments and associated meta-data for the last twenty-four hours, or those posted since the last retrieval time (if more recent). The API limits the returned comments to a maximum of 1,000.

  3. In order to track the comment activity on particular videos appearing intermittently in the most viewed feed, comments are also retrieved for those videos not in the current feed list that appeared in an earlier list from the previous forty-eight hours.

3.2 Data set properties

Data retrieval began on October 31st, 2011, and details of the videos and comments as of January 17th, 2012 can be found in Table 15.

Videos 6,407
Total comments 6,431,471
Comments marked as spam 481,334
Total users 2,860,264
Spam comment users 177,542
Table 1: Data set properties

An interesting feature of the API is the spam hint property provided within the video comment meta-data. This is set to true if a comment has previously been marked as spam, either by the spam filter or manually with the “Flag for spam” button available with each comment posted on a video’s page. However, this property cannot be considered reliable due to its occasional inaccuracy, where innocent comments can be marked as spam, while obvious spam comments are not marked as such. This will be demonstrated later in the results discussion. Similar evidence of this property’s unreliability was also encountered in earlier work [\citeauthoryearSureka2011].

Although the comment spam hint is used for approximate annotation of the data (Table 1), it is not relied upon as a label for the purposes of this evaluation. Other research in this area [\citeauthoryearBenevenuto et al.2009] performed manual label annotation of YouTube data for use in subsequent classification experiments. An accurate annotation process will be considered in future work.

4 Methodology

4.1 Comment processing and network generation

Our methodology requires the generation of a network to represent the comment posting activity of users to a set of videos. Initially, comments made during a specified time interval are selected from the data set discussed in the previous section. However, a number of pre-processing steps must be executed before an appropriate network can be generated similar to those in Figure 1.

Spammers try to obfuscate the text of comments from a particular campaign in order to bypass their detection by any filters. Obfuscation techniques include the use of varying amounts of additional characters (e.g. whitespace, Unicode newlines, etc.) within the comment text, or different textual formations (e.g. additional words, misspellings) of the same fundamental message. Some examples of these can be seen in the next section.

To counteract these efforts, each comment is converted to a set of tokens. During this process, stopwords are removed, along with any non-Latin-based words as the focus of this evaluation is English-language spam comments. Punctuation characters are also removed, and letters are converted to lowercase. A modified comment text is then generated from the concatenation of the generated tokens. As initial analysis found that spam comments can often be longer than regular comments, any texts shorter than a minimum length (currently 25 characters) are removed at this point. Although the campaign strategies under discussion here are concerned with attracting users to remote sites through the inclusion of URLs in comment text, comments without URLs are currently retained. This ensures the option of analysing other types of spam campaigns, such as those encouraging channel views, i.e. promoters [\citeauthoryearBenevenuto et al.2009], along with the behaviour of regular users.

A network can then be generated from the remaining modified comment texts. This network consists of two categories of node, users and videos. An undirected edge is created between a user and a video if at least one comment has been posted by the user on the video, where the edge weight represents the number of comments in question. For the moment, the weight is merely recorded but is not subsequently used when counting motifs within the network. To capture the relationship between the users involved in a particular spam campaign, undirected and unweighted edges are created between user nodes based on the similarity of their associated comments. Each modified (tokenized) comment text is converted to a set of hashes using the Rabin-Karp rolling hash method [\citeauthoryearKarp and Rabin1987], with a sliding window length of 3. A pairwise distance matrix, based on Jaccard distance, can then be generated from these comment hash sets. For each pairwise comment distance below a threshold (currently 0.6), an edge is created between the corresponding users if one does not already exist.

Afterwards, any users whose set of adjacent nodes consists solely of a single video node are removed. Since these users have commented on only one video, and are in all likelihood not related to any other users, they are not considered to be part of any spam campaign. The resulting network tends to consist of one or more large connected components, with a number of considerably smaller connected components based on videos with a relatively minor amount of comment activity. Finally, an approximate labelling of the user nodes is performed, where users are labelled as spam users if they posted at least one comment whose spam hint property is set to true. All remaining users are labelled as regular users. Although this can lead to label inaccuracies, the results in the next section demonstrate that such inaccuracies will be perceivable.

4.2 Network motif profiles

Once the network has been generated, a set of egocentric networks can be extracted. In this context, given that the focus is on user activity, an ego is a user node, where its egocentric network is the induced k-neighbourhood network consisting of those user and video nodes whose distance from the ego is at most k (currently 2). Motifs from size three to five within the egocentric networks are then enumerated using FANMOD [\citeauthoryearWernicke and Rasche2006]. A set of motif counts is maintained for each ego, where a count is incremented for each motif instance found by FANMOD that contains the ego.

A network motif count profile is then created for each ego. As the number of possible motifs can be relatively large (particularly if directed and/or weighted edges are considered), the length of this profile will vary for each network generated from a selection of comment data, rather than relying upon a profile with a (large) fixed length. For a particular generated network, the profiles will contain an entry for each of the unique motifs found in the entirety of its constituent egocentric networks. Any motifs not found for a particular ego will have a corresponding value of zero in the associated motif profile.

As mentioned previously, the work of Milo et al.  \shortciteMilo05032004 proposed the generation of a significance profile, where the significance of a particular motif was calculated based on its count in a network along with that generated by an ensemble of corresponding random networks. These profiles then permitted the subsequent comparison of different networks. In this work, the egocentric networks are compared with each other, and the generation of random ensembles is not performed. An alternative ratio profile  [\citeauthoryearWu, Harrigan, and Cunningham2011] is created for each ego, where the ratio value for a particular motif is based on the counts from all of the egocentric networks, i.e.:


Here, is the count of the motif in the ego’s motif profile, is the average count of this motif for all motif profiles, and is a small integer that ensures that the ratio is not misleadingly large when the motif occurs in only a few egocentric networks. To adjust for scaling, a normalized ratio profile is then created for each ratio profile with:


The generated set of normalized ratio profiles usually contain correlations between the motifs. Principal components analysis (PCA) is used to adjust for these, acting as a dimensionality reduction technique in the process. We can visualize the first two principal components as a starting point for our analysis. This is discussed in the next section.

5 Experiments and Results


Figure 2: Spatialization of the first two principal components of the normalized ratio profiles for Windows 10 and 11 (red nodes are users with comments marked as spam, beige nodes are all other users). Both spam campaigns are highlighted.

For the purpose of this evaluation, the experiments were focused upon tracking two particular spam campaigns that we discovered within the data set. The campaign strategies can be seen in Figure 1, i.e. a small number of accounts each commenting on many videos (Campaign 1), and a larger number of accounts each commenting on few videos (Campaign 2). A period of seventy-two hours was chosen where these campaigns were active, starting on November 14th, 2011 and ending on November 17th, 2011.

In order to track the campaign activity over time, this period was split into twelve windows of six hours each. For each of these windows, a network of user and video nodes was derived using the process described in the previous section. A normalized ratio profile was generated for each ego (user), based on the motif counts of the corresponding egocentric network. Principal components analysis was then performed on these profiles to produce 2-dimensional spatializations of the user nodes, using the first two components. These spatializations act as the starting point for the analysis of activity within a set of time windows.

5.1 Visualization and initial analysis

Having inspected all twelve six-hour windows, two windows containing activity from both campaigns have been selected for detailed analysis here. These are from November 17th, 2011; Window 10 (04:19:32 to 10:19:32) and Window 11 (10:19:32 to 16:19:32). Their derived network details can be found in Table 2.

Window Video nodes User nodes (spam) Edges
10 263 295 (107) 907
11 296 523 (137) 1627
Table 2: Network details for Windows 10 and 11

A spatialization of the first two principal components of the normalized ratio profiles for these windows can be found in Figure 2. Users posting at least one comment marked as spam (using the spam hint property) are in red, all other users are in beige. The points corresponding to the spam campaign users have been highlighted accordingly. From the spatializations, it can be seen that in both windows:

  1. The vast majority of users appear as overlapping points in larger clusters (on the right and left respectively).

  2. There is a clear distinction between the two different campaign strategies, as these points are plotted separately (both from regular users and each other).

  3. The inaccuracy of the spam hint comment property is demonstrated as the Campaign 2 clusters contain users not coloured in red, i.e. none of their comments were marked as spam (further details of these users can be seen in Figure 4). Similarly, the reverse is true with the larger clusters of regular users.

Apart from the highlighted campaign clusters, other spam nodes in the spatializations have been correctly marked as such. For example, the five users that are separated from the normal cluster in Window 10 (“Other spam users”) appear to be isolated spam accounts having similar behaviour to the Campaign 1 strategy, but on a smaller scale. This also applies to the single separated user in Window 11. They are not considered further during this evaluation as they are not part of a larger campaign.

Further analysis of Campaign 1 revealed that two and three users were active in Windows 10 and 11 respectively (five separate users), posting the following comments:

Three most cool things in the World for me before
1 ))))) Jordan–the super star
2 ))))) 66cheap. com–the cheapest shopping site
3 ))))) the iphone – best connector
Three Best things in the World for me now: ): ): ): ): )
1. Lily——My boyfriend!
2. 55cheap. com–the cheapest shopping site
3. the video above—- the most ironical and interesting
video I think:]:]:]:]:] :]:] :]:]

Figure 3: Tracking the recurring activity of Campaign 1 (left) and Campaign 2 (right) for all six-hour windows from 14th November 2011 to 17th November 2011, using a single discriminating motif for each campaign.

Although there are certain differences between these comments, they are clearly from the same campaign. This behaviour is also seen in both windows with Campaign 2, featuring a larger number of users, although there are fewer occurrences of identical comments. Nevertheless, a similarity is noticeable, for example:

Don’t miss this guys, the CEO of apple is releasing
ipads on Thursday:
dont miss out. November 17 - new apple ceo is shipping
out old ipad and iphones
Not a lie. Go to this webpage to see what I mean:\vatABm

Both of these comments are made by the same user in different windows. However, while the first comment was accurately marked as spam, the second was not. An assumption here could be that the URL in the first comment is on a spam blacklist, while the shortened URL in the second enables such a list to be bypassed. Similar shortcomings are discussed in earlier work [\citeauthoryearChhabra et al.2011].

5.2 Discriminating motifs

An inspection of the individual motif counts found that certain motifs have relatively higher counts for users involved in the spam campaigns, than those found for regular users. These motifs may be considered indicative of different campaign strategies, and a subset can be found in Table 3.

Campaign 1 Campaign 2
Table 3: A subset of discriminating motifs for different spam campaign strategies (user nodes are beige, video nodes are blue).

These discriminating motifs would appear to correlate with the existing knowledge of the campaign strategies. Campaign 1 consists of a small number of users commenting on a large number of videos, and so it would be expected that motifs containing only one user node with a large number of video node neighbours have higher counts for the users involved, as is the case here. The motifs considered indicative of Campaign 2 are more subtle, in that the number of user and video nodes is similar, with both user and video nodes present in the set of neighbours for a particular user. However, all three highlight the fact that users appear to be more likely to be connected to other users rather than videos. This makes sense given that with this campaign, a larger number of users tend to comment on a small number of videos each, and the potential for connectivity between users is higher given the similarity of their comments. These motifs would also appear to indicate that users in the campaign don’t comment on the same videos, as no two users share a video node neighbour.

Figure 3 contains plots for the counts of two of these motifs for each of the six-hour windows. The counts were normalised using the edge count for the corresponding window networks followed by min-max normalization. The fluctuation in counts across the windows appears to track the recurring periodic activity of these campaigns, as confirmed by separate analysis of the data set. This would appear to corroborate the bursty nature of spam campaigns [\citeauthoryearXie et al.2008, \citeauthoryearGao et al.2010].

Figure 4: Users associated with Campaign 1 (left) and Campaign 2 (right), having the highest counts for a single discriminating motif for each campaign from Window 11 (17th November 2011 10:19:32 to 16:19:32). Note how three of the users in Campaign 2 are coloured differently, i.e. none of their comments were marked as spam. Users not involved in the campaigns have been anonymized.

Finally, Figure 4 plots the user counts in descending order for these two motifs in Window 11. With the Campaign 1 motif (left), the first four users are involved and have considerably higher counts than the remaining users. There are also differences in counts between the campaign users themselves, indicating the most active users in this window. All users plotted for the Campaign 2 motif (right) are indeed involved. Three of the Campaign 2 users were coloured differently to the others, highlighting the fact that none of their comments were accurately marked as spam. These same three users can be seen in the right spatialization in Figure 2.

6 Campaign Analysis

Following the inspection of the discriminating motifs, the websites and domains associated with the comments posted by the campaign 1 users were then analyzed. The following domains were found in the data set in comments beginning with “Three Best things” and “Three most cool things” (as seen in the example comments listed in the previous section), and can be categorized as follows:

  1. National Football League (NFL) merchandise:,,

  2. Footwear:

  3. Wider range of merchandise (e.g. clothing, accessories):,,

It is quite clear that all of these sites are related given the high similarity between them, e.g. various index page titles containing the text “The Cheapest Shopping Site”, identical payment options and the same contact email address. There are also some inconsistencies in the HTML content, for example, some pages refer to and, and pages refer to Suspicious claims are also made, such as “SHOPOFNFL.COM was the online shop of NFL”. At first glance, looks different to the others, but further investigation reveals similarities such as the payment options. The domains appear to have been registered by the same person6. As has been previously identified as a known scam website7, it is safe to assume that all of these sites should be treated as such.

Further analysis of shows it to be an older site, as its About page alleges that it has been in operation for “17 years” since 1993. This would suggest that this scam has been in operation since 2010 at the very least. It appears that the About page details contain further inconsistencies, e.g., states that “In 2009, 78.8% of our annual revenue was from the international market…”, while, allegedly in operation for “18 years” since 1993 contains the same statement with merely a change in year: “In 2010, 78,8% of our annual revenue was from the international market…”.

A total of 24 different user accounts were used to send the associated comments found in the data set. Although some of these accounts have been suspended by YouTube, others remain active. The campaign appears to rotate the existing accounts for comment posting, and new accounts are created on a continual basis. The four accounts for this campaign listed in Figure 4 are currently active as of January 2012. Of these four, the oldest account was created in August 2011, while the most recent was created in October 2011.

7 Conclusions and Future Work

YouTube spam campaigns typically involve a number of spam bot user accounts controlled by a single spammer targeting popular videos with similar comments over time. We have shown that dynamic network analysis methods are effective for identifying the recurring nature of different spam campaign strategies, along with the associated user accounts. We have used a characterization of YouTube users in terms of motifs in the comment network to highlight the users in question. While the YouTube comment scenario could be characterized as a network in a number of ways, we use a network representation comprising user and video nodes, user-video edges representing comments and user-user edges representing comment similarity.

The discriminating power of these motif-based characterizations can be seen in the PCA-based spatialization in Figure 2. It is also clear from Figure 3 that histograms of certain discriminating motifs show the level of activity in the two campaign strategies over time. Furthermore, counts of these motifs in the egocentric networks of users highlight the associated accounts (Figure 4).

7.1 Future Work

For future experiments, it will be necessary to annotate the data set with spam/non-spam labels, or perhaps a more extensive annotation that considers the associated campaign strategies. Feature selection of a subset of motifs could then be performed along with subsequent user classification. The use of a subset of motifs is attractive, as it would remove the current requirement to count all motif instances found in the user egocentric networks, which can be a lengthy process.

8 Acknowledgements

This work is supported by 2Centre, the EU funded Cybercrime Centres of Excellence Network and Science Foundation Ireland under grant 08/SRC/I140: Clique: Graph and Network Analysis Cluster.


  5. The data set is available at


  1. Allan, Jr., E. G.; Turkett, Jr., W. H.; and Fulp, E. W. 2009. Using network motifs to identify application protocols. In Proceedings of the 28th IEEE Conference on Global Telecommunications, GLOBECOM’09, 4266–4272. Piscataway, NJ, USA: IEEE Press.
  2. Becchetti, L.; Boldi, P.; Castillo, C.; and Gionis, A. 2008. Efficient semi-streaming algorithms for local triangle counting in massive graphs. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08, 16–24. New York, NY, USA: ACM.
  3. Benevenuto, F.; Duarte, F.; Rodrigues, T.; Almeida, V. A.; Almeida, J. M.; and Ross, K. W. 2008a. Understanding Video Interactions in YouTube. In Proceedings of the 16th ACM International Conference on Multimedia, MM ’08, 761–764. New York, NY, USA: ACM.
  4. Benevenuto, F.; Rodrigues, T.; Almeida, V.; Almeida, J.; Zhang, C.; and Ross, K. 2008b. Identifying video spammers in online social networks. In Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web, AIRWeb ’08, 45–52. New York, NY, USA: ACM.
  5. Benevenuto, F.; Rodrigues, T.; Almeida, V.; Almeida, J.; and Gonçalves, M. 2009. Detecting spammers and content promoters in online video social networks. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09, 620–627. New York, NY, USA: ACM.
  6. Benevenuto, F.; Magno, G.; Rodrigues, T.; and Almeida, V. 2010. Detecting Spammers on Twitter. In Proceedings of the 7th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS).
  7. Boykin, P., and Roychowdhury, V. 2005. Leveraging social networks to fight spam. Computer 38(4):61 – 68.
  8. Cheng, X.; Dale, C.; and Liu, J. 2008. Statistics and Social Network of YouTube Videos. In The 16th International Workshop on Quality of Service (IWQoS ’08), 229 –238.
  9. Chhabra, S.; Aggarwal, A.; Benevenuto, F.; and Kumaraguru, P. 2011.$ocial: The phishing landscape through short urls. In Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, CEAS ’11, 92–101. New York, NY, USA: ACM.
  10. Gao, H.; Hu, J.; Wilson, C.; Li, Z.; Chen, Y.; and Zhao, B. Y. 2010. Detecting and characterizing social spam campaigns. In Proceedings of the 10th Annual Conference on Internet Measurement, IMC ’10, 35–47. New York, NY, USA: ACM.
  11. Grier, C.; Thomas, K.; Paxson, V.; and Zhang, M. 2010. @spam: The underground on 140 characters or less. In Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS ’10, 27–37. New York, NY, USA: ACM.
  12. Kamaliha, E.; Riahi, F.; Qazvinian, V.; and Adibi, J. 2008. Characterizing Network Motifs to Identify Spam Comments. In Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, 919–928. Washington, DC, USA: IEEE Computer Society.
  13. Karp, R. M., and Rabin, M. O. 1987. Efficient randomized pattern-matching algorithms. IBM J. Res. Dev. 31:249–260.
  14. Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; and Alon, U. 2002. Network Motifs: Simple Building Blocks of Complex Networks. Science 298(5594):824–827.
  15. Milo, R.; Itzkovitz, S.; Kashtan, N.; Levitt, R.; Shen-Orr, S.; Ayzenshtat, I.; Sheffer, M.; and Alon, U. 2004. Superfamilies of Evolved and Designed Networks. Science 303(5663):1538–1542.
  16. Mishne, G. 2005. Blocking Blog Spam with Language Model Disagreement. In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb.
  17. Paolillo, J. 2008. Structure and Network in the YouTube Core. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences, 156.
  18. Shen-Orr, S. S.; Milo, R.; Mangan, S.; and Alon, U. 2002. Network Motifs in the Transcriptional Regulation Network of Escherichia coli. Nature Genetics 31:1061–4036.
  19. Sureka, A. 2011. Mining User Comment Activity for Detecting Forum Spammers in YouTube. CoRR abs/1103.5044.
  20. Watts, D. J., and Strogatz, S. H. 1998. Collective dynamics of small-world networks. Nature 393(6684):440–442.
  21. Wernicke, S., and Rasche, F. 2006. FANMOD: A Tool for Fast Network Motif Detection. Bioinformatics 22(9):1152–1153.
  22. Wu, G.; Harrigan, M.; and Cunningham, P. 2011. Characterizing Wikipedia Pages using Edit Network Motif Profiles. In Proceedings of the 3rd International Workshop on Search and Mining User-Generated Contents, SMUC ’11, 45–52. New York, NY, USA: ACM.
  23. Xie, Y.; Yu, F.; Achan, K.; Panigrahy, R.; Hulten, G.; and Osipkov, I. 2008. Spamming botnets: Signatures and characteristics. In Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication, SIGCOMM ’08, 171–182. New York, NY, USA: ACM.
Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
Add comment
Loading ...
This is a comment super asjknd jkasnjk adsnkj
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test description