The development of information and communications technology in the 21st century has led to a shift in the distribution of information from traditional print media to electronic media. New means of informing the public have emerged, such as social networks and messengers, which made it possible to share information almost instantly and over long distances. In this regard, it has become a daunting task to analyze the patterns of information spreading, particularly, by studying information sources, revealing hidden interests and bias, identifying opinion leaders. The article presents methods for solving the problems of network analysis of identifying information dissemination patterns, determining «critical paths» of information dissemination by analyzing messages and news in the Telegram messenger.
Network
information resources are the main channel for obtaining information about what
is happening in the country and the world. The transition of traditional media
from paper to electronic form is taking place, some publishers have completely
abandoned the printed versions [1]. Due to the news abundance, many publishers
are moving to instant messaging platforms - such as Telegram, WhatsApp.
Instant messaging
platforms allow creating specialized channels both to inform the population,
and to receive feedback (using the function "comment" and graphic
response to the message) [2]. This method of conveying information is
characterized by zero cost of entering the platform, as an organization does
not bear any financial costs to maintain and support the resource.
Currently, plenty
of news agencies, individuals (bloggers), commercial and governmental
organizations, executive authorities have their own news channels in the Telegram
messenger. The number of users following such channels varies from a few dozens
to several million people.
The paper
investigates the information dissemination models on the Telegram instant
messaging platform. The object of study is an individual information message,
which can be distributed within closed environment infinitely. Besides Telegram
can be considered as one of the subtypes of the social network.
A significant
number of articles and books have been devoted to the study of the phenomenon
of social networks and methods of information dissemination [3, 4]. Separately,
we can figure out [5] a considered social network as an object.
Researchers
distinguish several classes of "game-theoretic" models of social
networks:
•
Models of mutual
awareness.
•
Models of coordinated
collective action.
•
Communication models.
•
Stability models.
•
Models of informational
influence and management.
•
Models of information
confrontation.
The article
considers models of informational influence and management that have the unique
property of having “opinion leaders” [3]. We have developed a tool for
collecting textual information and metadata related to reposts and reactions to
information messages and methods of their graph analysis.
Considering
Telegram as one of the subtypes of a social network, we highlight that the
nodes are entities such as personalities, channels and groups, and the edges
are informational messages, membership of participants in groups, etc. [6, 7].
The first
objective is to collect information from Telegram channels. This task can be
solved using two resources - Telegram and TGStat website, which provides
statistics about Telegram.
The joint use of
the resources allows ensuring the completeness of the collected messages and
metadata necessary for the development of the model of information distribution
in the network. The collection process is shown below
(Figure 1).
There are three major
ways to interact with Telegram: using apps (mobile and desktop), using a
browser, or using API. The last method is used, as it provides the most
complete and the fastest data collection. According to Telegram API each
message is characterized by 29 attributes, but the most significant in the
research are:
•
Id – message identifier
in the channel,
•
peer_id –
channel information,
•
date – publication date
and time,
•
message –
publication text,
•
fwd_from – information
about forwarded messages.
The “fwd_from” attribute
consists of 10 fields, the most significant are:
•
date – publication date
and time,
•
from_id – information
about the primary source,
•
channel_post – message
identifier in the primary source.
Telegram has more
than 300,000 channels, for the purposes of the experiment, the analysis was
conducted on the sample of 30 channels, with a total audience of more than 3.5
million users. Channels were selected based on their audience and were not
linked by their thematic focus.
After implementing
the data collection, a data model was developed to build the directed graph.
The nodes are the channels, the arcs (directed edges) indicate message
forwarding from one channel to another. The node of the graph is defined by the
following fields:
•
Id – node identifier,
•
label – channel ID
according to Telegram API,
•
name – channel name.
An arc is defined by the following fields:
•
Source – identifier of
the start node,
•
Target – identifier of
the end node,
•
Type – edge type
(directed),
•
Weight – weight of the
node,
•
Date – date of message
forwarding.
The dissemination
of information message in Telegram can be considered from two perspectives: in
terms of causes and predictions. Adrien Guille et al. [3] describe these models
for distribution of messages between users, but this approach can also be
applied to Telegram if we consider individual channels as users. We hypothesize
that some channels are more interconnected than others. It is possible to
identify this correlation, as well as to obtain a predictive model, by
comparing distribution paths. So, we should consider their structure.
In terms of graph
theory, a propagation path can be considered as an oriented tree, where each
parent can have several children. The tree, as a graph, can be both numbered by
vertices and unnumbered.
Considering the
propagation path as a graph, we can use the following metric to compare two
paths with each other [8]:
(1)
where
–
graph i, msg(
)
– maximum common subgraph, |
| –
the order of graph i
The disadvantages
of this metric are that the algorithms to find the maximum common subgraph are
labor-intensive, and that it does not take into account the graph structure.
There are other metrics [9, 10], but they also do not take into account the
type of a graph.
To determine the
similarity of objects we will use edit distance [11]. Edit distance is a metric
defined as the minimum number of one-character operations (insertion, deletion,
replacement) needed to turn one sequence into another. In case of node-numbered
trees, the node number will be the Telegram channel Id, so edit distance
between two dissemination paths for different topics will be large. Therefore,
in order to compare paths that propagate through different channels, unnumbered
channels are used – in this case more general conclusions can be drawn.
Edit distance for
oriented unlabeled trees can be calculated based on their string representation
[12].
In this paper we
consider the approach of visual graph analysis. To perform this we will map
each object to a graph according to the following rules:
1. Each node
will have a spread depth.
2. Each arc
will have a weight inversely proportional to the forwarding time.
395 795
informational messages were collected in the Telegram messenger over the 3
months period, then they were converted for graph representation, visualization
was performed using the tool Gephi [13]. That tool was chosen due to the
user-friendly interface and the possibility of adding a dynamic component.
First, the graph
for 30 channels and their output to external sources was plotted
(Figure 2).
In this figure channels for which messages were
collected are highlighted in green, and those channels from which redirection
took place are highlighted in red. Node size is directly proportional to the
number of messages that were forwarded from this channel.
Figure
2.
Interconnection of information channels
The graph shows a
certain homogeneity of the information field in the center, which characterizes
strong interrelations among the participants of the information field. Wide
range of "buds" on the periphery indicates the number of channels
being monitored. It is also possible to make preliminary conclusions about the
amount of unique content generated by the channel. The more
"dead-end" edges indicate the less unique content produced. However, in
order to confirm this statement, it is necessary to conduct additional research
to assess the ratio of content within the information channel, which is one of
the extensions of the authors' research.
Note the presence
of reflexive arcs (as in the highlighted fragment in
Figure 2),
that is, some channels refer to their own messages.
This can confirm the authority of the channels, but negatively affects the
overall perception of the information field.
The second stage was
the expansion of the set of channels for message collecting to 35 pieces.
Channels with the largest number of links were added to the sample, based on
the analysis of the graph
(Figure 2),
and then new graphs were plotted. For them the
reflexive connection was removed and additional color design was added to
improve perception.
First, a graph is
constructed to obtain overall statistics by channel (Figure 3). There are
several particularly interesting aspects for researchers.
First, there are
apparent opinion leaders (“8”, “3”, “23”), which are predominantly referred to
by other channels, stand out. On the other hand, it can be traced that the
major source of information for the channel “4” is the channel “5”, while the
rest of the messages are taken from channels to which no other authoritative
channel is affiliated.
Figure
3.
Interconnection of information channels at the second stage
Figure
4.
Dynamic
graph of information channels interconnection
The second graph
was constructed with the dynamic component by the date of forwarding to the
target channel
(Figure 4).
This approach allows
estimating the flow of forwarded messages for different time intervals. The
data sample presents the 3 months period of 2022, which is shown in the figure
(by converting timestamp to date), we can see the burst of publications in the
middle of the sample (about 20%), which indicates a significant newsworthy event
that occurred in this time interval. In addition, we can note the timing of the
interaction between the isolated channels (for example, “16”, “33”, “34”) and
more central channels.
The limitation of
this method is that the size of nodes (which is proportional to the number of
links per channel) does not change its size in different time intervals.
The distribution
of an information message can be described using a probabilistic model. In this
case, one of the possible ways for the analysis is the cascade model of infection
[4]. Since the work is aimed at obtaining a predictive model and detection of
anomalous phenomena in the graph, and the propagation time can take from
several minutes to several days, this model is insufficient for the analysis.
On the other hand, propagation paths can be compared by edit distance and
visually. In this case, the graphical representation, theoretically, allows quick
highlighting of anomalies on a single example.
The information
signal propagation in Telegram is shown using Gephi. The label of edges is
calculated as the time difference (in minutes) between the publication of a
message in channel i and its forwarding in channel j, and the weight of an edge
is determined by the following formula:
(2)
where
k
– proportionality
coefficient (take k=10),
–
the time difference (in minutes) between the publication of a message in
channel I and its forwarding in channel j.
170 objects were selected
for analysis. Out of 170 objects, 48 pairs (0.4%) were found (on average 12
vertices in one graph) in which the editorial distance satisfies the condition:
(3)
Let's consider two
graphs
(Figure 5,
Figure 6).
On each of the above graphs a color
scheme is used to separate nodes by the depth of propagation, and the numerical
designation is the edge label.
Figure
5.
Hierarchical representation of the distribution graph
Figure
6.
Propagation
graph build by ForceAtlas2 algorithm
Two features can
be emphasized on the first graph. First, some posts are forwarded almost
immediately, while others are forwarded much later. On the other hand, some
posts are forwarded to the exact minute, although they take quite a long time.
For example, the first graph highlights edges with forwarding times of 231
minutes at level 2; with forwarding times of 74 and 533 minutes at level 3.
Since there are 3 pairs of completely identical edges out of 18 edges (by time
and absence of child elements) there is an assumption about the connection of
channels-receivers in pairs with each other or about intentional forwarding of
information signals.
The second graph
represents one of the largest propagation networks, containing 165 nodes and a
maximum depth of 7. The ForceAtlas 2 visualization algorithm has adequately
placed the nodes by propagation depth. This example highlights the chain of
nodes that corresponds to the main propagation. In the majority of cases,
messages are forwarded from these nodes, indicating that these channels are
authoritative. The identified chain is associatively similar to the critical
path of the Gantt chart [14,15] used in project management theory.
Let's
introduce the concept of critical path of signal propagation as a sequence of
nodes participating in the propagation of information signal, providing the
greatest information coverage.
In
addition, let us introduce the characteristic of time. For this purpose, a
graph is built, where each node has a label “time since the beginning of
propagation” (in hours)
(Figure 7).
This graph contains two critical paths
which contain 8 nodes each, the time before forwarding to the end nodes was 858
hours (35 days) and 778 hours (32 days).
Figure
7.
Critical paths of information signal propagation
Social networks
and instant messaging platforms represent a field for research on information
signals. The tasks of such research are to determine the nature of propagation,
to reveal the relationships between individual subjects/objects, and to build the
predictive model of propagation.
Two tasks were
solved in the work: the analysis of information dissemination in Telegram for
the most popular channels (30 channels in the initial sample) and the
consideration of individual information signals. The analysis of channel
interaction potentially allows identifying "opinion leaders",
"aggregators" of information and bursts of forwarding activity.
The distribution
of individual information messages can be described by graph theory, but
comparing the constructed “trees” causes difficulties. Graphs can be compared
by calculating the edit distance, but the impossibility of using temporal
characteristics (forwarding time) imposes a limitation on the use of this
metric. On the other hand, the visual representation of such objects allows
highlighting critical places, such as identical ways of message propagation
(the same propagation time). A separate class of tasks is the study of “critical
paths” of signal propagation, which will allow solving a wide range of tasks
related to management and confrontation in information networks, including
identification of primary sources, ways of information dissemination, “opinion
leaders”.
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