Pro ling Less Active Users in Online Communities

Abstract

Our research is focused on the study of social interactions of online community users, especially in business-oriented social networking services like LinkedIn or Habrahabr. The general aim of the work is to design methods for pro ling of discussion participants within groups according to their interaction patterns. One of our goals is to make the approach independent from the language of communication, that is why we build our analysis on the comments graph and do not use information from the posts content. This paper suggest FCA based approach to proling less active users for which not much data is available and statistical analysis is not applicable.

Keywords

• online community
• communication graph
• user pro les

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Social Internet development unveiled great research potential for network analysis which includes the analysis of relationships and ows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities.

This paper focuses on the behaviour patterns of the members of social networks groups (communities) of interest. In these online groups, users on a regular basis can publish information or news that called posts, and interact with each other by commenting or liking them.

The general goal of this work is to provide a method of pro ling group users by analysing the group interaction graph. An interaction graph is a graph where vertices correspond to users and edges represent relation \user A comments or likes post of user B".

Today almost every social media site provides an API for easy data retrieval. Application programming interface (API) is the set of routines, protocols, and tools for building software applications using the obtained data. In order to retrieve the graph of social interaction we use data sets collected from businessoriented social networking service LinkedIn and Habrahabr (leading Russian blog on Information Technology topics).

In this paper we continue research described in [ 1 ], which is also dedicated to the task of pro ling online community users. The method proposed in that paper is based on clustering users according to statistical characteristics of their communication patterns.

Clustering based on statistical characteristics allows one to study the communication patterns, but it is not applicable to users with low activity for which not enough data is available. Our study shows that actively involved users constitute approximately 2% of a community, while more than half of the community member could be classi ed as \observers" (see [ 1 ]). That motivates us on designing a separate technique for pro ling less active members of an online community.

The paper is organized as follows. Section 2 contains the review of relevant related works organized according to the used approach. Section 3 describes the data set. Section 4 summarizes achieved and anticipated results. In Section 5 we conclude and discuss the possible applications of our work. 2

Related work

Relationship is a central concept of the science of Social Network Analysis. Our race, ethnicity, background and personality | all in uence our behaviour and interact. Thus, the behavioural patterns analysis in online communities can provide the information about the user that is not explicit in his or her pro le page (and obviously cast some light on the principles of social behaviour in online networks).

There are di erent types of relationships between people: friendship, trust, in uence, or con ict, dislike etc. In [ 2 ] authors provide several types of relationships in social networks including (1) binary and valued relationships, (2) symmetric and asymmetric relationships, and (3) multimodal relationships. Examples of binary and valued relationships are \Sam follows Ann on Facebook" and \Alex retweeted 4 tweets from Mary" respectively. Following or reposting on Twitter, Facebook or LinkedIn are asymmetric relationships by de nition, but a follow-back tie can exist, thus symmetrizing them. An example of symmetric relation is \Ann and Bob have common interests". Multimodal relationships are interactions between actors of di erent types people possess information, group adds people, and so on. In our study, we analyze all these three types of relationships between group users.

The task of user pro le modelling consists of many subtasks and approaches, such as content-based methods [ 3 ], the island method [ 2 ], researching of users friend- or following-connections, or tracing user activity [ 4 ] etc. Typically, the majority of proposed pro ling methods combine di erent techniques.

An example of Twitter users pro ling is presented in [ 5 ]. Authors study demographic estimation algorithms based on users tweets and community relationships. They propose a hybrid community-based and text-based method where demographics of Twitter users are estimated by tracking the tweet history and clustering the followers/followings. The method estimates wide varieties of demographics such as gender, age, area etc. The authors also consider users with few tweets such as followers of corporate accounts.

In [ 6 ] authors suggest a generic model for user classi cation in social media with application to Twitter. Analyzing the users behaviour, linguistic content and the network structure of the users Twitter feed they develop the method of automatic inferring the values of user attributes such as political orientation or ethnicity. Machine learning approach is used relying on four general feature classes: user pro le, user tweeting behaviour, linguistic content of user messages and user social network features. The paper presents experimental results on 3 tasks with di erent characteristics: ethnicity identi cation, political a liation detection and detecting a nity for a particular business.

A weakly supervised approach to user pro le extraction from Twitter is also suggested in [ 7 ]. In addition to traditional linguistic features, this approach also takes into account network information, o ered by social media. Authors use users pro les from social media websites such as Facebook or Google Plus as a distant source of supervision for extraction of their attributes from usergenerated text. They test the algorithm on three attribute domains including spouse, job and education and results demonstrate accurate predictions for users attributes based on tweets.

Unlike previous mentioned works, article [ 4 ] focus just on user activity, ignoring the content of messages a user exchanged. Authors take into consideration both social interactions and tweeting patterns of microblogging integrating service Twitter, which allow pro ling users according to their activity patterns. According to the investigation, there are 75 % of the users in their appropriate cluster, which can be classi ed with a 0.9 assignment probability. Clusters are characterized by a set of statistical features relating user activity, network structure and dynamic patterns. Furthermore, the authors propose three algorithms to analyze the impact of content posted by a user.

In [ 8 ] authors use modelling user pro le to predict the pro le of another user in the network. They gather ne-grained data from two social networks and try to infer user pro le attributes. The article proposes a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks based on the fact, that users with common attributes are more likely to be friends and often form dense communities. Results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users.

The expertise retrieval task of user pro ling is also mentioned in [ 9 ]. In this work, the topical pro ling task is decomposed into two stages: (1) discovering and identifying possible knowledge areas, and (2) measuring the persons competency in each of these areas.

Our research has the same goal as the previous mentioned works | to provide a method of pro ling users. Main task of this paper is to suggest an approach for pro ling of less active users, which usually form the majority of any online community. Since we have little data for these members, statistical analysis of their behaviour is not possible. That is why we turn to FCA tools.

The idea to apply formal concept analysis to social network analysis is not new, see e.g. [ 10 ] or [ 11 ]. Usually the technique is used for network clustering and detecting communities. Our goal is slightly di erent, we assume that a community is already given. We target at detailed description of roles of di erent users in this community.

Method of retrieving groups of websites users with similar behaviour using Formal Concept Analysis presented in [ 12 ]. Authors propose to construct a taxonomy based on users visits of di erent pages of websites. The problem of big number of concepts is solved by applying the stability index [ 13 ] to the lattice concepts.

Extension of using the stability index (not only in terms of intent stability, but also in terms of extent stability) to taxonomy construction described in [ 14 ]. For instance, in this work authors study the dataset from research by Davis, Gardner and Gardner [ 15 ] which features ladies attending particular events in a small Mississippi town in the 1930s. By constructing stabilised lattice (according to extent) authors found the core members in groups. 3

Description of the data set

In our work we use two data sets. The rst one is communication graph retrieved from Habrahabr blogging site for several most popular topics. The second includes communication graphs for several LinkedIn groups. 3.1

Habrahabr data

HabraHabr (http://habrahabr.ru/) is the most popular Russian blog service devoted to Information Technology. Currently we work with communication graphs for the most active topics including "Algorithms", "Big Data", "High performance computing", "Information security" and others. For a given topic the dataset is a single table with the following columns: { Post Id { Post author { Comment Id { Comment author { Parent comment Id { Time stamp Also, for conveniece we added some derived values like comment depth, number of child comments etc.

Data set for "Big data" community in CSV format is available at the project page on GitHub, see https://github.com/ryavorsky/HabraGraph. 4 4.1

Users pro ling

3.2

LinkedIn data

Business-oriented social networking service LinkedIn, http://www.linkedin.com, allows users to create pro les and interact with each other in an online social network, which may represent real-world professional relationships. LinkedIn also supports the formation of interest groups that are, generally, employment related, although the majority of topics are covered mainly around professional and career issues. Currently we work with communication graphs for the largest groups related to the topic "Bioinformatics". The dataset has the same structure: Post Id, Post author, Comment Id, Comment author, Like author, Time stamp, and some derived characteristics, such as the number of child comments and its depth in the thread.

Clustering according to the statistical characteristics

In this paper we continue research on pro ling online community users described in [ 1 ]. Firstly, a set of the user attributes that can be computed using community comment graph and post comment graphs is listed. They are: the number of people that leave comment to the user and were commented by the user, the number of posts the user wrote and commented, average depth of the user's comment and how often the user's comment was a terminal. These attributes re ect the user's communication style in online discussions.

The clustering allowed us to gure out the following user types: 1. Silent stars (2 users). Authors of popular posts who do not participate in the discussions. 2. Communicative stars (2 users). Authors of popular posts who are actively involved in the discussions. 3. Active chatters (2% of users). Participants who leave many comments, and reply to almost every comment on their posts. 4. Idle chatters (2% of users). People who write few comments, but usually their comments support the subsequent debate. 5. Socializers (5% of users). Users who do not produce many comments, although the number of people their talk with is notably high. 6. Investigators (15% of users). Participants who communicate with many people within very narrow discussion (few blog posts). 7. Concluders(22% of users). Participants, who produce little comments and quite often their comment is the last one in the discussion branch. 8. There is also one more type of user - observers (more than 50%) who are the most inactive users: each one leaved no more than 3 comments.

It can be seen that less active users represent bigger part of community. That is why the goal of the current work is to provide method of detection of dependencies between users with di erent activity rate. In other words we want to know to whom among the active users the less active users are similar. 4.2

Pro ling of less active users

As it was mentioned above, the task of analyzing and pro ling of user behaviour is rather straightforward for more active users, when a lot of data is available.

For the other part of online community, the majority of less active members, we suggest to describe the user pro le in terms of similarity to one or several preselected benchmarks, key users of the community with well-known behavioural patterns.

In more details the suggested procedure is the following. First, select a small number of key users of the studied community. Second, build the object-attribute table, in which rows (objects) are all group users and columns (properties) are benchmark pro les (key users). Then use use FCA tools to compute the lattice of formal concepts. Finally, conclude that activity pattern of user user1 could be described in terms of intersection of few benchmarks, e. g. core user1 and core user3. 4.3

Core users

There are many di erent ways to determine the set of benchmarks. In our work we use the notion of communication graph core [ 16,17 ]. The picture on g. 1 shows 3-core for users-posts graph (that is the largest subset of users and posts, in which each user left comments in at least three posts and each post has comments from at least three users) corresponding to \big data" community at Habrahabr.ru platform.

Restricting our focus with the 3-core helps us to lter out blog posts which are not very relevant to the main community topic, and also gure out users, which play central role in the group communications.

Consider for example an irrelevant post, which produced quite intensive discussion. We can detect its irrelevance by the fact that core users did not participated in the thread. Formally, to classify a post as a core one we require that at least 3 core users participate in the discussion.

Similarly, there might be a user, who went into long comments exchange in a single thread or left few remarks in some rather irrelevant discussions. User with such a behaviour usually is interpreted as a casual visitor, not a core one. To be included into the community core we require that the user should participate in at least 3 core discussions. 4.4

Why FCA As it was already mentioned above, the main goal of this work is to design an approach for describing pro les for majority of less active members of an online community. Usually we have very little data for such users, a couple of comments or so. That is why classi cation according to numerical characteristics suggested in [ 1 ] hardly makes sense. The users will be classi ed as \inactive" and that's it.

In this paper we suggest to use information about the particular topics, which attracted a user. That data has \object-property" type, so we turn to FCA.

The formal context is de ned as follows. Assuming user is a group user and benchmark is a core user we say that object user has property benchmark if users user and benchmark together participated in a post discussion. For the example of \big data" community mentioned above the formal context table has 13 properties (the number of core users) and hundreds of objects (for all the other community members).

We use FCA tools (Concept Explorer [ 18 ] and FCArt [ 19,20 ]) to build the lattice of the formal context. As a result, the set of formal concepts is given (see g. 2). Each formal concept has a set of objects (extent). These users are similar to each other and their pro le could be speci ed in terms of the benchmarks, the set of core users.

By combining the 3-core graph and the lattice we can get a visual map of the community, see g. 3.

Also, in applications we can use the resulting formal concepts for introducing the corresponding links between the user pro les. Indeed, for a user with low activity the pro le will be almost empty due to lack of statistics. The links from this empty pro le to more detailed pro les of most similar core users will help to get at least some information about the user interests. 5

Conclusion

The paper describes work in progress on developing a universal tool for automated building of pro les for online community users. The proposed method is based on the user activity in the process of posting, liking and commenting group posts.

To make the approach suitable for the analysis of di erent online communities, the approach does not use information from the user pro le or content analysis. Thus, it is based on user activity and his/her skills to interact with other group participants.

In order to classify less active online group members the method based on retrieving formal concepts with core users as attributes is suggested. The results can be used to extend the functionality of the groups with the detailed description of the pro le of participants and the nature of their interaction, which in turn should help to understand users behaviour.

The developed method can be applied to any online community.

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