Author: Robert Ackland, Bryan Gertzel, Francisca Borquez

# Introduction to vosonSML

### VOSON Lab, Australian National University

#### Robert Ackland, Bryan Gertzel, Francisca Borquez

#### 04 July, 2025

The following guide provides an introduction to using vosonSML, which is available both on GitHub and CRAN. More resources are available on the VOSON Lab website (vosonSML and training materials). For a full list of functions, please refer to the reference page. The companion package to `vosonSML` is VOSON Dashboard, which provides an R/Shiny graphical user interface for data collection (via `vosonSML`), network and text analysis.

To use `vosonSML`, you first need to load it into the session:

    library(vosonSML)

There are three steps involved in data collection and network creation using `vosonSML`. These are:

1. Authenticate with the platform API, using the function `Authenticate()`
2. Collect data from the API and store it for later use, using `Collect()`
3. Create networks from the collected data, using `Create()` and `Graph()`

# YouTube

## Authenticating with the YouTube API

To collect YouTube data, it is necessary to first create a Google app with access to the YouTube Data API via the Google APIs console and generate an associated API key. The following shows the creation of a YouTube access token by passing a Google developer API key to the `Autnenticate()` function:

    # create auth object with api key
    youtubeAuth <- Authenticate("youtube", apiKey = "xxxxxxxx")

The YouTube access token can optionally be saved to disk for use in a later session:

    saveRDS(youtubeAuth, file = "youtube_auth")

The following loads into the current session a previously-created authentication object:

    youtubeAuth <- readRDS("youtube_auth")

## Collecting Data from YouTube

The YouTube video IDs (the part after “=” in the YouTube URL) are required in order to collect YouTube comment data. These IDs can either be manually provided or automatically extracted from the URLs:

    videoIDs <- c(
      "xxxxxx",
      "https://www.youtube.com/watch?v=xxxxxxxx",
      "https://youtu.be/xxxxxxxx")
    )

The character vector containing the YouTube video IDs or URLs is passed as a parameter to the `Collect()` function (the following code also shows the YouTube access token being piped to `Collect()`). In the following example, we are collecting comments from a YouTube video titled “Australia bushfires - a national catastrophe | DW News”, which was uploaded by the German Deutsche Welle news service on 5th January 2020. The comment data were collected on 10th January 2020: the total number of comments at that time was over 1100, but we are using the `maxComments` parameter to collect a maximum of 500 top-level comments (and all the reply comments to these top-level comments).

    videoID <- "https://www.youtube.com/watch?v=pJ_NyEYRkLQ"
    youtubeData <- youtubeAuth |>
      Collect(videoID, maxComments = 500, writeToFile = TRUE)

The `Collect()` function takes the following arguments (when used for collecting YouTube data): `credential` (object generated from `Authenticate()` with class name “youtube” (above we pass this via the pipe), `videoIDs` (character vector specifying one or more youtube video IDs), `maxComments` (numeric integer specifying how many top-level comments to collect from each video), `writeToFile` (whether to write the returned dataframe to disk as an `.rds` file; default is FALSE), and `verbose` (whether to output information about the data collection; default is FALSE).

`Collect()` returns an R dataframe with the following structure (data have been modified to preserve anonymity):

    > str(youtubeData)
    Classes ‘dataource’, ‘youtube’ and 'data.frame':    603 obs. of  12 variables:
     $ Comment              : chr  "xxxxx"
     $ AuthorDisplayName    : chr  "xx" "xx" "xx" "xx"
     $ AuthorProfileImageUrl: chr  "https://xx" "https://xx" "https://xx"
     $ AuthorChannelUrl     : chr  "http://xx" "http://xx" "http://xx" "http://xx"
     $ AuthorChannelID      : chr  "xx" "xx" "xx" "xx"
     $ ReplyCount           : chr  "0" "0" "0" "0"
     $ LikeCount            : chr  "0" "0" "0" "0"
     $ PublishedAt          : chr  "2020-01-10T02:23:43" "2020-01-09T20:56:23"
                                   "2020-01-09T20:44:00" "2020-01-09T19:31:32"
     $ UpdatedAt            : chr  "2020-01-10T02:23:43" "2020-01-09T20:56:23"
                                   "2020-01-09T20:44:00" "2020-01-09T19:31:32"
     $ CommentID            : chr  "xx" "xx" "xx" "xx"
     $ ParentID             : chr  NA NA NA NA
     $ VideoID              : chr  "pJ_NyLQ" "pJ_NyLQ" "pJ_NyLQ" "pJ_NyLQ"

### Importing saved collection data from file

If you are reading a previously saved `writeToFile` YouTube dataframe from disk, you simply need to use the `readRDS` function:

    # read dataframe from file
    youtubeData <- readRDS("2020-09-26_095354-YoutubeData.rds")

## Creating YouTube Networks

It is currently possible to create two types of networks using YouTube data: (1) actor network and (2) activity network.

### Actor Network

In the YouTube actor network the nodes are users who have commented on videos (and the videos themselves are included in the network as special nodes) and the edges are the interactions between users in the comments. We can distinguish a top-level comment, which is a comment that is directed to a video from a reply comment, which is a comment directed to a top-level comment. The YouTube user interface does not allow a user to direct a reply to another reply. However, users can achieve the “reply to a reply” functionality by starting their comment with the username of the person that they are replying to (and they often prepend the username with “@”). So there will be an edge from user i to user j if i replied to a top-level comment authored by j or else i prepended their comment with j’s username.

    actorNetwork <- youtubeData |> Create("actor") |> AddText(youtubeData)
    actorGraph <- actorNetwork |> Graph(writeToFile = TRUE)

`Create("actor")` returns a named list containing two dataframes named “nodes” and “edges” (the following has been modified to preserve anonymity):

    > actorNetwork
    $nodes
    # A tibble: 522 x 3
       id                       screen_name                         node_type
       <chr>                    <chr>                               <chr>
     1 xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxx                      actor
     2 xxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
    [snip]
    # … with 512 more rows

    $edges
    # A tibble: 604 x 6
       from      to       video_id comment_id   edge_type vosonTxt_comment
       <chr>     <chr>    <chr>    <chr>        <chr>     <chr>
     1 xxxxxxxx… VIDEOID… pJ_NyEY… xxxxxxxxxxx… comment   "xxxxx"
     2 xxxxxxxx… VIDEOID… pJ_NyEY… xxxxxxxxxxx… comment   "xxxxx"
    [snip]
    # … with 594 more rows

    attr(,"class")
    [1] "list"       "network"    "actor"      "youtube"    "voson_text"

Note that in the above, `AddText()` was used to add the comment text data to the network dataframe, stored as an edge attribute. Also, note that there is an optional parameter `replies_from_text` that can be passed to `AddText()` when used with YouTube network creation, to extract the “reply to reply” comments.

This list is then passed to `Graph()`, which returns an `igraph` graph object. Remember that it is possible to achieve the above using a single line of code:

    actorGraph <- youtubeData |> Create("actor") |> AddText(youtubeData) |> Graph()

The following is an an annonymised summary of the `igraph` graph object.

    > actorGraph
    IGRAPH 79e5456 DN-- 522 604 --
    + attr: type (g/c), name (v/c), screen_name (v/c), node_type (v/c),
    | label (v/c), video_id (e/c), comment_id (e/c), edge_type (e/c),
    | vosonTxt_comment (e/c)
    + edges from 79e5456 (vertex names):
    [1] xxxx->VIDEOID:pJ_NyEYRkLQ
    [2] xxxx->VIDEOID:pJ_NyEYRkLQ
    [snip]
    + ... omitted several edges

The YouTube actor network node contains a graph attribute `type` (set to “youtube”). The node attributes are: `name` (Channel ID, which is YouTube’s unique user ID), `screen_name` (the users displayed name), `node_type` (‘actor’ or ‘video’) and `label` (a concatenation of the ID and screen name). The edge attributes are: `video_id` (the ID of the video for which the data have been collected), `comment_id` (the ID of the comment), `edge_type` (whether the edge is a ‘comment’ i.e. top-level comment, ‘reply-comment’ i.e. reply to top-level comment or reply to reply or ‘self-loop’, which is a special edge connecting the video to itself, as a means of including text posted with the video). In the above example, because of our earlier use of `AddText()`, there is also an edge attribute `vosonTxt_comment` which is the text associated with the comment, reply or video.

The example YouTube actor network contains 522 nodes and 604 edges. The following indicates that there were 500 top-level comments (we constrained the collection to this number), 103 replies to top-level comments (note: we did not use `AddText()` to collect replies embedded within the text), and there is the single self-loop from the video to itself.

    > table(E(actorGraph)$edge_type)
       comment reply-comment     self-loop
           500           103             1

We can visualize this network, using red to identify the video nodes.

    # change color of nodes with type video to red and others grey
    V(actorGraph)$color <- ifelse(
      V(actorGraph)$node_type == "video", "red", "grey"
    )

    # open and write plot to a png file
    png("youtube_actor.png", width = 600, height = 600)
    plot(actorGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
    dev.off()

YouTube actor network (red node is video)

The following creates a sub-network containing only the replies to top-level comments. In removing the other edges (top-level comments and the self-loop) we create a number isolate nodes (nodes with no connections) that we also remove. We have also used red to indicate the people who have written comments containing particular terms that have been present in the online commentary about the bushfires.

    # removed edges that are not of type reply-comment
    g2 <- delete.edges(
      actorGraph, which(E(actorGraph)$edge_type != "reply-comment")
    )

    # check number of isolates
    > length(which(degree(g2) == 0))
    [1] 417

    # remove isolates
    g2 <- delete.vertices(g2, which(degree(g2) == 0))

    # get node indexes for the tails of edges that have comments containing
    # words of interest change the indexed node colors to red and others grey
    V(g2)$color <- "grey"
    ind <- tail_of(
      actorGraph,
      grep("arson|backburn|climate change", tolower(E(g2)$vosonTxt_comment))
    )
    V(g2)$color[ind] <- "red"

    # open and write plot to a png file
    png("youtube_actor_reply.png", width = 600, height = 600)
    plot(g2, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
    dev.off()

YouTube actor network - only actors sending or receiving replies

Finally, the `AddVideoData()` function supplements the network data with additional video information.

    actorNetwork_withVideoInfo <- actorNetwork |> AddVideoData(youtubeAuth)

`AddVideoData()` returns a named list containing three dataframes named “nodes” (identical to the dataframe contained in the list `actorNetwork` in the example able), “edges” (this has three additional columns: “video_title”, “video_description”, “video_published_at”) and a new dataframe “videos” (the following has been modified to preserve anonymity):

    > actorNetwork_withVideoInfo
    $nodes
    # A tibble: 522 x 3
       id                       screen_name                         node_type
       <chr>                    <chr>                               <chr>
     1 xxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
     2 xxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxx                       actor
    [snip]
    # … with 512 more rows

    $edges
    # A tibble: 604 x 9
       from  to    video_id comment_id edge_type vosonTxt_comment video_title
       <chr> <chr> <chr>    <chr>      <chr>     <chr>            <chr>
     1 xxxx… xxxx… pJ_NyEY… xxxxxxxxx… comment   xxxxxxxxxxxx … Australia …
     2 xxxx… xxxx… pJ_NyEY… xxxxxxxxx… comment   "xxxx"         Australia …
    [snip]
    # … with 594 more rows, and 2 more variables: video_description <chr>,
    #   video_published_at <chr>

    $videos
    # A tibble: 1 x 6
     VideoID  VideoTitle  VideoDescription VideoPublishedAt ChannelID ChannelTitle
     <chr>    <chr>       <chr>            <chr>            <chr>     <chr>
    1 pJ_NyEY… Australia … "As Australia ba… 2020-01-05T12:3… UCknLrEd… DW News

    attr(,"class")
    [1] "list"             "network"          "actor"            "youtube"
    [5] "voson_text"       "voson_video_data"

It should also be noted that `AddVideoData()` can optionally substitute references to the video ID in the “nodes” and “edges” dataframes with the video publishers channel ID (this is done by setting the parameter `actorSubOnly` to TRUE.

### Activity Network

In the YouTube activity network, nodes are either comments or videos (videos represent a starting comment).

    activityNetwork <- youtubeData |> Create("activity") |> AddText(youtubeData)
    activityGraph <- activityNetwork |> Graph()

`Create("activity")` returns a named list containing two dataframes named “nodes” and “edges” (the following has been modified to preserve anonymity).

    > activityNetwork
    $edges
    # A tibble: 603 x 3
       from                       to                  edge_type
       <chr>                      <chr>               <chr>
     1 xxxxxxxxxxxxxxxxxxxxxxxxxx VIDEOID:pJ_NyEYRkLQ comment
     2 xxxxxxxxxxxxxxxxxxxxxxxxxx VIDEOID:pJ_NyEYRkLQ comment
    [snip]
    # … with 593 more rows

    $nodes
    # A tibble: 604 x 8
       id    video_id published_at updated_at author_id screen_name node_type
       <chr> <chr>    <chr>        <chr>      <chr>     <chr>       <chr>
     1 xxxx… pJ_NyEY… 2020-01-10T… 2020-01-1… xxxxxxxx… xxxxxxxxxx… comment
     2 xxxx… pJ_NyEY… 2020-01-09T… 2020-01-0… xxxxxxxx… xxxxxxxxxx… comment
    [snip]
    # … with 594 more rows, and 1 more variable: vosonTxt_comment <chr>

    attr(,"class")
    [1] "list"       "network"    "activity"   "youtube"    "voson_text"

Note that in the above, `AddText()` was used to add the comment text data to the network dataframe, stored as a node attribute. This list is then passed to `Graph()`, which returns an `igraph` graph object (this has been anonymised).

    IGRAPH 02664d1 DN-- 604 603 --
    + attr: type (g/c), name (v/c), video_id (v/c), published_at (v/c),
    | updated_at (v/c), author_id (v/c), screen_name (v/c), node_type
    | (v/c), vosonTxt_comment (v/c), label (v/c), edge_type (e/c)
    + edges from 02664d1 (vertex names):
    [1] xxxx->VIDEOID:pJ_NyEYRkLQ
    [2] xxxx->VIDEOID:pJ_NyEYRkLQ
    [3] xxxx->VIDEOID:pJ_NyEYRkLQ
    [4] xxxx->VIDEOID:pJ_NyEYRkLQ
    [5] xxxx->VIDEOID:pJ_NyEYRkLQ
    [6] xxxx->VIDEOID:pJ_NyEYRkLQ
    + ... omitted several edges

The YouTube activity network contains a graph attribute `type` (set to “youtube”). The node attributes are: `name` (character string ID number for the comment or video), `video_id` (character string ID of the video for which the comments collected - in this example, “pJ_NyEYRkLQ”), `published_at` (timestamp of when the comment was published, this is `NA` for the video itself), `updated_at` (timestamp of when a comment was updated), `author_id` (user’s Channel ID), `screen_name` (user’s display name), `node_type` (whether the node is a ‘comment’ i.e. top-level comment, ‘reply-comment’ i.e. reply to top-level comment or reply to reply or ‘video’), `vosonText_comment` (the comment text, `NA` for the video), `label` (concatenation of `name` and `screen_name`). The edge attributes `edge_type` which is ‘comment’ for all edges connecting a top-level comment to the video, and ‘reply-comment’ for all other edges.

The example YouTube activity network contains 604 nodes and 603 edges. The following is an `igraph` visualization of the network, where the video is indicated by a red node, and blue indicates comments that include one of the following terms: “arson”, “bakcburn”, “climate change”.

    # set all video node colors to red and others to grey
    V(activityGraph)$color <- "grey"
    V(activityGraph)$color[which(V(activityGraph)$node_type == "video")] <- "red"

    # get node indexes of comments that contain terms of interest
    # set their node colors to blue
    ind <- grep(
      "arson|backburn|climate change", tolower(V(activityGraph)$vosonTxt_comment)
    )
    V(activityGraph)$color[ind] <- "blue"

    # open and write plot to a png file
    png("youtube_activity.png", width = 600, height = 600)
    plot(activityGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
    dev.off()

YouTube activity network (red node is video, blue nodes are comments mentioning particular terms)

# Reddit

The Reddit collection in `vosonSML` is based on the approach used in the RedditExtractoR package.

## Authenticating with the Reddit API

The `vosonSML` does not require Reddit API credentials to be provided. However, to keep the workflow consistent with the other data sources, we still need to create a “dummy” access token, using the `Authenticate()` function (see below).

## Collecting Data from Reddit

To collect Reddit comment data, first construct a character vector containing the post URL(s).

    myThreadUrls <- c(
      "https://www.reddit.com/r/xxxxxx/comments/xxxxxx/x_xxxx_xxxxxxxxx/",
      "https://www.reddit.com/r/xxxxxx/comments/xxxxxx/x_xxxx_xxxxxxxxx/"
    )

This character vector is then passed as an argument to the `Collect()` function. In the example below, a post relating to the politics around the Australian bushfires was used: https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/. This post was created on 7th January 2020 and by the time of data collection (10th January), it had attracted over 4000 comments. The maximum number of comments available for retrieval is 500 per thread or post.

Reddit has implemented a feature in their latest site re-design to branch off into new threads, when a thread reaches a breadth (diameter) of 10 comments. These appear as ‘Continue this thread’ links in thread discussions on the reddit site, and as new listing markers within the collected thread data. `vosonSML` follows these links with additional thread requests and collects comments from those as well, capturing a more complete data set, as the limit of 500 comments applies to each ‘new’ thread.

    myThreadUrls <- "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/"
    redditData <- Authenticate("reddit") |>
                  Collect(threadUrls = myThreadUrls, writeToFile = TRUE)

The `Collect()` function takes the following arguments (when used for collecting Reddit data): `credential` (an object generated from `Authenticate()` with class name “reddit” (above we pass this via the pipe), `threadUrls` (character vector of Reddit thread urls), `waitTime` (a numeric vector giving the time range in seconds to select random wait url collection requests; default is `c(3, 10)` i.e. random wait between 3 and 10 seconds), `ua` (User-Agent string; default is `option("HTTPUserAgent")` as set by vosonSML, `writeToFile` (whether to write the returned dataframe to file as an `.rds` file; default is FALSE), `verbose` (whether to output information about the data collection; default is TRUE).

The `Collect()` function returns a `tibble` dataframe (this output has been anonymised):

    > str(redditData)
    Classes ‘tbl_df’, ‘tbl’, ‘datasource’, ‘reddit’ and 'data.frame':
      767 obs. of  22 variables:
     $ id              : int  1 2 3 4 5 6 7 8 9 10 ...
     $ structure       : chr  "1" "4_1_1_1_1_1_1_1_1_1" "4_1_1_4_2_1_1_1_1_1" ...
     $ post_date       : chr  "2020-01-07 14:34:58" "2020-01-07 14:34:58" ...
     $ post_date_unix  : num  1.58e+09 1.58e+09 1.58e+09 1.58e+09 1.58e+09 ...
     $ comm_id         : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
     $ comm_date       : chr  "2020-01-07 19:11:10" "2020-01-07 21:04:05" ...
     $ comm_date_unix  : num  1.58e+09 1.58e+09 1.58e+09 1.58e+09 1.58e+09 ...
     $ num_comments    : int  4435 4435 4435 4435 4435 4435 4435 4435 4435 4435 ...
     $ subreddit       : chr  "worldnews" "worldnews" "worldnews" "worldnews" ...
     $ upvote_prop     : num  0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 ...
     $ post_score      : int  45714 45714 45714 45712 45714 45710 45720 45712 ..
     $ author          : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
     $ user            : chr  "xxxx" "xxxx" "xxxx" "xxxx" ...
     $ comment_score   : int  1904 136 17 13 9 9 125 4 6 12 ...
     $ controversiality: int  0 0 0 0 0 0 0 0 0 0 ...
     $ comment         : chr  "xxxx...
     $ title           : chr  "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" "Australia’s leaders deny link between climate change and the country’s devastating bushfires" ...
     $ post_text       : chr  "" "" "" "" ...
     $ link            : chr  "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" "https://www.theglobeandmail.com/world/article-australias-leaders-unmoved-on-climate-action-after-devastating-2/" ...
     $ domain          : chr  "theglobeandmail.com" "theglobeandmail.com" "theglobeandmail.com" "theglobeandmail.com" ...
     $ url             : chr  "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" "https://www.reddit.com/r/worldnews/comments/elcb9b/australias_leaders_deny_link_between_climate/" ...
     $ thread_id       : chr  "elcb9b" "elcb9b" "elcb9b" "elcb9b" ...

### Importing saved collection data from file

If you are reading a previously saved `writeToFile` Reddit dataframe from disk, you simply need to use the `readRDS` function.

    redditData <- readRDS("2020-09-26_095354-RedditData.rds")

## Creating Reddit Networks

It is currently possible to create two types of networks using Reddit data: (1) actor network and (2) activity network.

### Actor Network

In the Reddit actor network, nodes represent users who have posted original posts and comments and the edges are the interactions between users in the comments i.e. where there is an edge from user i to user j if i writes a comment that replies to user j’s comment (or the original post).

The following creates a Reddit actor network with comment text as an edge attribute (as above, this can be achieved in a single line of code, but we split it into two lines to better explain the objects that are created).

    actorNetwork <- redditData |> Create("actor") |> AddText(redditData)
    actorGraph <- actorNetwork |> Graph(writeToFile = TRUE)

`Create("actor")` returns a named list containing two dataframes named “nodes” and “edges” (the following has been modified to preserve anonymity):

    > actorNetwork
    $nodes
    # A tibble: 439 x 2
          id user
       <int> <chr>
     1     1 xxxxxxxxxx
     2     2 xxxxxxxxxxxxxx
    [snip]
    # … with 429 more rows

    $edges
    # A tibble: 768 x 8
        from    to subreddit thread_id comment_id comm_id vosonTxt_comment   title
       <int> <int> <chr>     <chr>        <dbl> <chr>   <chr>                <chr>
     1     1   439 worldnews elcb9b           1 xxxxxxx "xxxxxxxxxxxxxxxxxxx NA
     2     2    73 worldnews elcb9b           2 xxxxxxx "xxxxxxxxxxxxxxxxxxx NA
    [snip]
    … with 758 more rows

    attr(,"class")
    [1] "list"       "network"    "actor"      "reddit"     "voson_text"

Note that in the above, `AddText()` was used to add the comment text data to the network dataframe, stored as an edge attribute. This list is then passed to `Graph()`, which returns an `igraph` graph object.

    > actorGraph
    IGRAPH 5a5d5b9 DN-- 439 768 --
    + attr: type (g/c), name (v/c), user (v/c), label (v/c), subreddit
    | (e/c), thread_id (e/c), comment_id (e/n), comm_id (e/c),
    | vosonTxt_comment (e/c), title (e/c)
    + edges from 5a5d5b9 (vertex names):
     [1] 1 ->439 2 ->73  3 ->113 4 ->120 5 ->120 6 ->17  7 ->194 8 ->20  9 ->20
    [10] 10->165 11->165 12->1   13->2   14->3   15->4   16->5   17->6   18->7
    [19] 19->8   20->9   21->10  22->11  23->12  2 ->13  24->3   7 ->18  25->23
    [28] 26->2   3 ->24  27->18  28->1   29->2   18->27  1 ->28  30->2   31->7
    [37] 25->1   32->2   33->31  34->1   2 ->32  35->7   25->34  36->2   7 ->35
    [46] 37->1   38->2   39->7   40->1   41->2   42->7   43->1   2 ->41  44->7
    + ... omitted several edges

The Reddit actor network contains a graph attribute `type` (set to “reddit”). The node attributes are: `name` (sequential ID number for actor, generated by `vosonSML`), `user` (Reddit handle or screen name)) and `label` (a concatenation of the ID and screen name). The edge attributes are: `subreddit` (the subreddit from which the post is collected), `thread_id` (the 6 character ID of the thread or post), `comment_id` (sequential ID number for comment, generated by `vosonSML`). There is also an edge attribute `title`, which is set to `NA` for all comments except the comment representing the original post. Further note that the original post is represented as a self-loop edge from the user who authored the post (and this is how the post text can be accessed, as an edge attribute), however with the Reddit actor network, there is no `edge_type` attribute. Finally, because we used `AddText()` in the above example, there is also an edge attribute `vosonTxt_comment` which is the text associated with the comment, or original post.

The example Reddit actor network contains 439 nodes and 768 edges. The following is a visualization of the actor network, where the author of the post is indicated by a red node, and blue nodes indicate those people who mentioned “arson” or “starting fires” in at least one of their comments.

    # set node color of original post to red based on presence of title edge
    # attribute set other node colors to grey
    V(actorGraph)$color <- "grey"
    V(actorGraph)$color[tail_of(
      actorGraph, which(!is.na(E(actorGraph)$title))
    )] <- "red"

    # get node indexes for the tails of edges that have comments containing
    # words of interest set their node colors to blue
    ind <- tail_of(
      actorGraph,
      grep("arson|starting fires",
           tolower(E(actorGraph)$vosonTxt_comment))
    )
    V(actorGraph)$color[ind] <- "blue"

    # open and write plot to a png file
    png("reddit_actor.png", width = 600, height = 600)
    plot(actorGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
    dev.off()

Reddit actor network (red node is author of post, blue nodes are users who mentioned arson)

### Activity Network

In the Reddit activity network, nodes are either comments and/or initial thread posts and the edges represent replies to the original post, or replies to comments.

    activityNetwork <- redditData |> Create("activity") |> AddText(redditData)
    activityGraph <- activityNetwork |> Graph(writeToFile = TRUE)

`Create("activity")` returns a named list containing two dataframes named “nodes” and “edges” (the following has been modified to preserve anonymity):

    > activityNetwork
    $nodes
    # A tibble: 768 x 10
       id    thread_id comm_id datetime     ts subreddit user  node_type
       <chr> <chr>     <chr>   <chr>     <dbl> <chr>     <chr> <chr>
     1 elcb… elcb9b    xxxxxxx 2020-01… 1.58e9 worldnews xxxx… comment
     2 elcb… elcb9b    xxxxxxx 2020-01… 1.58e9 worldnews xxxx… comment
    [snip]
    # … with 758 more rows, and 2 more variables: vosonTxt_comment <chr>,
    #   title <chr>

    $edges
    # A tibble: 767 x 3
       from                       to                       edge_type
       <chr>                      <chr>                    <chr>
     1 elcb9b.1                   elcb9b.0                 comment
     2 elcb9b.4_1_1_1_1_1_1_1_1_1 elcb9b.4_1_1_1_1_1_1_1_1 comment
    [snip]
    # … with 757 more rows

    attr(,"class")
    [1] "list"       "network"    "activity"   "reddit"     "voson_text"

Note that in the above, `AddText()` was used to add the comment text data to the network dataframe, stored as a node attribute. This list is then passed to `Graph()`, which returns an `igraph` graph object.

    > activityGraph
    IGRAPH 09e30ea DN-- 768 767 --
    + attr: type (g/c), name (v/c), thread_id (v/c), comm_id (v/c),
    | datetime (v/c), ts (v/n), subreddit (v/c), user (v/c), node_type
    | (v/c), vosonTxt_comment (v/c), title (v/c), label (v/c), edge_type
    | (e/c)
    + edges from 09e30ea (vertex names):
    [1] elcb9b.1                  ->elcb9b.0
    [2] elcb9b.4_1_1_1_1_1_1_1_1_1->elcb9b.4_1_1_1_1_1_1_1_1
    [3] elcb9b.4_1_1_4_2_1_1_1_1_1->elcb9b.4_1_1_4_2_1_1_1_1
    [4] elcb9b.4_1_1_4_3_1_1_1_3_1->elcb9b.4_1_1_4_3_1_1_1_3
    [5] elcb9b.4_1_1_4_3_1_1_1_3_2->elcb9b.4_1_1_4_3_1_1_1_3
    + ... omitted several edges

The Reddit activity network contains a graph attribute `type` (set to “reddit”). The node attributes are: `name` (string showing position of the comment in the thread), `date` (date when the comment was authored, in DD-MM-YY format), `subreddit` (the subreddit from which the post is collected), `user` (Reddit handle or screen name of the user who authored the comment or post), `node_type` (‘comment’ or ‘thread’), `title` (`NA` for all nodes except that representing the original post), `label` (a concatenation of `name` and `user`). Because we used `AddText()` in the above example, there is also a node attribute `vosonTxt_comment` which is the text from the comment, or original post. The edge attributes is `edge_type` which is ‘comment’ for all edges.

The example Reddit activity network contains 768 nodes and 767 edges. The following is a visualisation of the network, where the post is indicated by a red node, and blue indicates those comments that include the words “arson” or “starting fires”.

    # set original post node colors to red based on a node type of thread
    # set other node colors to grey
    V(activityGraph)$color <- "grey"
    V(activityGraph)$color[which(V(activityGraph)$node_type == "thread")] <- "red"

    # get node indexes for nodes that have comment attributes containing words of interest
    # set their node colors to blue
    ind <- grep("arson|starting fires", tolower(V(activityGraph)$vosonTxt_comment))
    V(activityGraph)$color[ind] <- "blue"

    # open and write plot to a png file
    png("reddit_activity.png", width = 600, height = 600)
    plot(activityGraph, vertex.label = "", vertex.size = 4, edge.arrow.size = 0.5)
    dev.off()

Reddit activity network (red node is intial post, blue nodes are comments mentioning arson)

# Merging Collected Data

Data that was collected at different times, used different collect parameters or was saved to multiple files can be merged by using functions that operate on dataframes. The data from `Collect` is output in `tibble` (dataframe) format and provided each collected data set are from the same social media type can be combined using the `rbind` function.

In the examples below or cases that involve large datasets, it can sometimes be more efficient or timely to substitute optimized functions such as `dplyr::bind_rows` for `rbind` or `data.table::rbindlist` instead of `do.call("rbind", list)`.

## Merging from multiple Collect operations

Data collected in the same session can be merged using the `Merge` function.

## Merging files in a directory

If there are many data collections to be merged from file `vosonSML` has a `MergeFiles` function.

## Creating a network and graph from merged data

Once the data is merged then it can then simply be passed to the `Create` function to create a network as per the usual `vosonSML` work flow.

# VOSON Dashboard

It is possible to import a network created using `vosonSML`, and saved as a “.graphml” file, into `VOSON Dashboard`. However, if you have created a categorical node attribute in the network and wish to plot networks in `VOSON Dashboard` with node colour reflecting the node attribute, then the node attribute name has to be pre-pended with “vosonCA_”. This let’s `VOSON Dashboard` know the attribute is to be treated as categorical.

The following network is a Twitter subnetwork, specifically the giant component in a reply network, with red nodes indicating those users who tweeted using the word “bushfire”. It was accomplished by creating a node attribute “tweetedBushfires”. For `VOSON Dashboard` to recognise this node attribute, it has to be named “vosonCA_tweetedBushfires”. The following code creates a new node attribute with this name, and sames the network as a graphml file:

    V(g3)$vosonCA_tweetedBushfires <- V(g3)$tweetedBushfires
    write.graph(g3, "g3.graphml", format = "graphml")

The following shows a screenshot of `VOSON Dashboard` with this network loaded and the “tweetedBushfires” attribute has been seleced to be reflected in the node colour.

VOSON Dashboard with Twitter network of users who tweeted using the word “bushfire”

# Acknowledgements

`vosonSML` and `VOSON Dashboard` are developed and maintained at the Virtual Observatory for the Study of Online Networks (VOSON) Lab at the Australian National University.

`vosonSML` was originally released on CRAN in November 2015 as the package `SocialMediaLab` (Timothy Graham was the lead developer), with the significantly revised and renamed `vosonSML` being released on CRAN in July 2018 (Bryan Gertzel is the lead developer).

We acknowledge the contributions of Chung-hong Chan who implemented a revised UI (involving `magrittr` pipes) in the original `SocialMediaLab` package and Xiaolan Cai who has contributed to documentation.