Aljaž Zrnec and Dejan Lavbič. 2018. Social network aided plagiarism detection, British Journal of Educational Technology (BJET), 48, pp. 113 - 128.

Abstract

The prevalence of different kinds of electronic devices and the volume of content on the Web have increased the amount of plagiarism, which is considered an unethical act. If we want to be efficient in the detection and prevention of these acts, we have to improve today’s methods of discovering plagiarism. The paper presents a research study where a framework for the improved detection of plagiarism is proposed. The framework focuses on the integration of social network information, information from the Web, and an advanced semantically enriched visualization of information about authors and documents that enables the exploration of obtained data by seeking of advanced patterns of plagiarism. To support the proposed framework, a special software tool was also developed. The statistical evaluation confirmed that the employment of social network analysis and advanced visualization techniques led to improvements in the confirmation and investigation stages of the plagiarism detection process, thereby enhancing the overall efficiency of the plagiarism detection process.

Keywords

Social Network Analysis, Plagiarism detection process, Plagiarism visualization, Assessment, Facilitation, Learning management systems, Student-centredness, Case study

1 Introduction

The widespread availability of computers and mobile devices and the volume of content on the Web have changed the approaches to both teaching and learning processes. Simultaneously, the amount of plagiarism has increased enormously over the last few years due to the aforementioned changes. The act of plagiarism is defined as the unethical action of copying someone else’s work (Youmans 2011), and is usually considered as an offense, therefore we also have to improve the current plagiarism detection processes to be able to cope with the increasing amount of plagiarism in a more efficient way.

Culwin and Lancaster defined the Four-Stage Plagiarism Detection Process (FSPDP) (Culwin and Lancaster 2001) used to systematically search for plagiarisms in a given set of documents focusing not only on similarity detection. FSPDP consists of four stages: collection, analysis, confirmation and investigation. Usually, all stages were performed by a human investigator, but with the advent of different plagiarism detection methods supported by computers, the first two stages in this process can be fully automated, and the latter two can only be partly automated (Makuc 2013). The effectiveness of detection depends not only on the similarity engine (Ali, Abdulla, and Snásel 2011; Hage, Rademaker, and Vugt 2010) used in the second stage, but also on the rate of automation of the latter two stages in the process. Any similarity in the second stage, which is considered as positive, is further reviewed in the investigation stage. For a submission to be judged to contain plagiarism, the confirmation stage (3rd stage) must be completed, where the submission is examined and verified by a human investigator. This stage can also be fully automated, but false positive and false negative results may occur.

Today, most of the approaches (Ali, Abdulla, and Snásel 2011) to the detection of plagiarism are focused on the first two stages, namely collection and analysis, leaving the investigator to perform the latter stages manually. That was the motivation behind conducting research work to propose a novel approach focusing on social aspects of potential plagiarists, by taking into account their social network connections, activities and information from the Web, to support investigator’s work in the third and the fourth stages of FSPDP, thereby making the plagiarism detection process more efficient. We believe that the plagiarism detection process can be improved by reducing the number of manual examinations of potentially plagiarized work. This could be achieved by the employment of new visualization techniques that enable a semantically enriched view of the relationships between possible plagiarists.

The paper is organized as follows. In the second section, we present the related work and propose a solution. In the third section, we describe our Social Plagiarism Detection Framework and supportive software tool. In the fourth section, we present the evaluation method for assessing the approach and discuss the obtained results. Finally, in the last section, we conclude the paper and discuss the possibilities for future research.

3 Social plagiarism detection framework

3.1 Description of proposed system

With the introduction of the Social Plagiarism Detection Framework (SPDF), we focus on the latter stages of the plagiarism detection process, namely in confirmation (3rd stage) and investigation (4th stage) as depicted in Figure 3.1.

Social Plagiarism Detection Framework (SPDF)

Figure 3.1: Social Plagiarism Detection Framework (SPDF)

The main contributions of our approach are as follows:

  • integration of social network information and information from the Web that facilitates the plagiarism detection process; and
  • an advanced semantically enriched visualization (semantic graph, co-occurrence matrix) of information about authors and documents that enables the exploration of data in search of advanced patterns of plagiarism.

The additional steps of SPDF and advantages compared with existing approaches based only on content similarity are depicted in Figure 3.2. Steps 1 - 5 are generally performed in content similarity matching in the plagiarism detection process, while we introduce steps 6 - 10.

Contribution of SPDF compared with existing approaches

Figure 3.2: Contribution of SPDF compared with existing approaches

In the confirmation stage, the system evaluates the content similarity report (Figure 3.2, step 5) provided in the analysis stage (Figure 3.2, steps 3 - 4) and performs an additional evaluation of general search engine results (Figure 3.2, step 9) and connections between authors on social networks (Figure 3.2, step 7). In the case of ambiguity, the investigator is provided with an option to review the social network analysis results. Based on all given information in the context (content similarity and connections between users on social networks), the investigator can confirm or reject pairwise plagiarism. The main benefit of our approach is the improved ranking of potential pairwise plagiarisms where social information as well as information from the Web are taken into account, thereby minimizing the effort required by the investigator in the confirmation stage. We argue, and provide a comprehensive evaluation of our findings in the fourth section, that the impact of social information is statistically significant in the plagiarism detection process.

3.2 Plagiarism detection framework definition

We can define \(P\) and \(D\) as nonempty sets of people and documents respectively

\[\begin{equation} P = \{ p \text{ | } p \text{ is a person} \} \tag{3.1} \end{equation}\] \[\begin{equation} D = \{d \text{ | } d \text{ is a document, written by } p \in P\} \tag{3.2} \end{equation}\]

where \(W\) is a set of pairs \(\langle p, d \rangle\) with \(p\) as an author of a document \(d\)

\[\begin{equation} W = \{ \langle p, d \rangle \text{ | } \exists p \in P \land d \in D : p \text{ is an author of } d \} \tag{3.3} \end{equation}\]

The set \(W\) is the result (Figure 3.2, step 2) of the collection stage (Figure 3.2, step 1) and is the direct input to the analysis stage (Figure 3.2, step 3). For this purpose, we can further define content similarity between documents

\[\begin{equation} CS = \{ \langle d_i, d_j, s_{ij} \rangle \text{ | } \exists d_i, d_j \in D \land s_{ij} \in \left ( 0, 1 \right ] : s_{ij} \text{ is a share of content from } d_i \text{ in } d_j \} \tag{3.4} \end{equation}\]

as a set of pairs of documents \(d_i\) and \(d_j\), with directed content similarity \(s_{ij}\) between documents (Figure 3.2, step 4).

By the integration of social network information in the plagiarism detection process, we intro- duce two measures: \(SN\) (Figure 3.2, step 6) and \(SE\) (Figure 3.2, step 8).

\(SN\) represents the connections between users on social networks considering pairwise actions between users. We define the following types of actions \(T(A) = \{ f^{\rightarrow }, f^{\leftrightarrow}, s^{\rightarrow} \}\) as (directed) follow \(f^{\rightarrow}\), (undirected) mutual follow \(f^{\leftrightarrow}\) and (directed) support \(s^{\rightarrow}\) (eg, share, comment, reply, retweet, favorite, like, \(+1\) etc).

When considering various social networks, we classify them into two distinct categories regarding user connections. The first group considers links between users as directed (unilateral following of users, eg, Twitter, Google+, etc), while the other group employs undirected links (users mutually confirm following, eg, Facebook, LinkedIn, etc). We can define the connections between users on social networks

\[\begin{equation} SN = \{ \langle p_i, p_j, A_{ij} \rangle \text{ | } \exists p_i, p_j \in P \land \exists a^{(ij)}_{k} \in A_{ij} : a \text{ is an action between } p_i \text{ and } p_j \} \tag{3.5} \end{equation}\]

as a set of triples of people \(p_i\) and \(p_j\), with set of actions \(A_{ij}\) between people \(p_i\) and \(p_j\). Actions are further defined as set of triples \(A_{ij} = \{ a_k^{(ij)} = \langle nt, act, w \rangle \}\), where \(nt\) is the social network (eg, Facebook, Twitter, LinkedIn, Google+, etc), \(act\) is activity (eg, follow, share, comment, like, etc) and \(w\) is a user-defined weight of specific action.

When determining pairs \(\langle p_i, p_j \rangle\) of connected people, a fuzzy search, implementing the Levenshtein distance algorithm, is performed that requires further action by the investigator in the case of ambiguity with multiple account and/or people matching.

We also introduce a set of related items from general search engine \(SE\) between people \(p_i\) and \(p_j\) (Figure 3.2, step 8) as

\[\begin{equation} SE = \{ \langle p_i, p_j, KW_{ij}, n \rangle \text{ | } \exists p_i, p_j \in P, n \in \mathbb{N} : n \text{ is number of related items} \} \tag{3.6} \end{equation}\]

where \(n\) is the number of relevant search results involving people \(p_i\) and \(p_j\), using a set of keywords \(KW_{ij}\). This set of keywords \(KW_{ij}\) between pairs \(\langle p_i, p_j \rangle\) of connected people, is user defined per assignment as follows:

\[\begin{equation} KW_{ij} = KW_{p_i} \cup KW_{p_j} \cup KW_{assignment} \tag{3.7} \end{equation}\]

where \(KW_{p_i}\) and \(KW_{p_j}\) are keywords related to a person’s information (eg, name, surname, etc.) and \(KW_{assignment}\) is a set of assignment-related keywords to narrow down the result set.

In the process of plagiarism detection, the goal is to define the set of pairs of documents \(DP\), where plagiarism has been confirmed

\[\begin{equation} DP = \{ \langle d_i, d_j \rangle \text{ | } \exists d_i, d_j \in D : d_i \text{ is a plagiat of } d_i \} \tag{3.8} \end{equation}\]

When using existing approaches, the investigator, who performs the plagiarism detection process, tries to identify elements of \(DP\), while considering \(CS\) and some tacit knowledge \(TK\) by investigation. The results of the confirmation and investigation stages can be defined as a function \(check_{woSocio}\), performed by the investigator

\[\begin{equation} check_{woSocio} : CS \times TK \rightarrow DP \tag{3.9} \end{equation}\]

The investigator evaluates the content similarity report consisting of pairwise documents and decides on classifying the event as confirmed, rejected or not-checked plagiarism.

When our proposed approach is utilized (Figure 3.3), the following function \(check_{wSocio}\) is defined

\[\begin{equation} check_{wSocio} : CS \times TK \times SN \times SE \rightarrow DP \tag{3.10} \end{equation}\]

that besides content similarity, also considers the social information of document authors (Figure 3.2, steps 6 - 9).

Ranked table of pairwise possible plagiarism

Figure 3.3: Ranked table of pairwise possible plagiarism

Figure 3.3 depicts a table of pairwise potential plagiarisms that the investigator has to examine in order to confirm or reject them. The investigator is provided with the full information about content similarity (\(CS\)), social network connectivity (\(SN\)) and general search engine matching (\(SE\)). The weighted average of the aforementioned factors between people \(p_i\) and \(p_j\) is defined as

\[\begin{equation} Total_{ij} = w_{CS} \cdot cs_{d(p_i) d(p_j)} + w_{SN} \cdot SN_{ij} + w_{SE} \cdot se_{ij} \tag{3.11} \end{equation}\]

where \(d(p_i)\) and \(d(p_j)\) are documents authored by people \(p_i\) and \(p_j\) respectively.

The weights \(w_{CS}\), \(w_{SN}\) and \(w_{SE}\), where \(\sum_i w_i = 1\), are user defined per assignment and allow investigators to define the importance of individual plagiarism detection factors (\(CS\), \(SN\) and \(SE\)).

To enable investigators to have an overview of factor values and their distribution (Figure 3.3), we introduce an indication of clusters (confirmed, rejected and not-checked possible plagiarism) for individual factors. It is available as an interval with the minimum and maximum values for the selected factor, where colors (red, orange and green for confirmed, not-checked and rejected potential plagiarism respectively) are used to depict mean values for individual clusters.

Furthermore, we argue that the employment of \(check_{wSocio}\) is more straightforward than \(check_{woSocio}\) and enables the investigator to perform the confirmation stage more efficiently. This results in a reduction of the total number of documents suspected to contain plagiarism that the investigator has to manually review and confirm or reject the occurrence of plagiarism. For evaluation purposes, the supporting tool has been developed to test and compare the aforementioned scenarios.

3.3 Limitations

With the introduction of SPDF, the limitations of the proposed approach also have to be considered. The major limitation of the approach is the dependency on publicly available data from social networks. If we want the approach to be as much efficient as possible, the authors (users) should have publicly accessible profiles on several observable social networks. In the case of Facebook, it is interesting that the requirement is not so difficult to achieve, as studies about the user identity presentation (user profile) on Facebook show that users are willing to provide substantial amounts of personal data (Gross and Acquisti 2005; Wilson, Gosling, and Graham 2012). Although the awareness of privacy and security issues has increased over the past few years, several studies have revealed that many users still have publicly accessible profiles (Mazur 2010; McKnight, Lankton, and Tripp 2011).

The second limitation is that the approach is suitable for smaller groups of authors (eg, in educational sectors or classes at University, High School, etc) because it requires gathering user data from social networks. Access to this kind of data is usually realized by using application programming interfaces (APIs) which are generally limited in terms of which and what data can be accessed and how often the data can be retrieved (Rieder 2013). Facebook, for example, is very restrictive in terms of which data can be accessed, and it also determines the request frequency, making the possibility of analyzing large set of users very impractical. Facebook, Twitter, LinkedIn and others also reserve the right to close or modify these APIs, which represent an additional limitation to the proposed approach.

The approach also raises some ethical questions as it anticipates the automatic gathering of an author’s data from social networks (Zimmer 2010). The authors of analyzed documents are not active participants in the collection of their social data because they could provide inaccurate data about social connections and their activities on social networks. Therefore, they should be warned about this approach used for plagiarism detection that uses automatic inquiries about their profiles before their assignments are checked. However, it is also important to point out that all the information gathered about users is publicly available.

3.4 Plagiarism detection assistant tool

To support the proposed process, the plagiarism detection assistant (PDA) tool was developed in which the following functionalities are supported:

  • creating and managing projects,
  • integration of existing plagiarism detection tools,
  • automatic acquisition of social network information and general search engine results,
  • confirming/rejecting assignments, and
  • advanced visualization.

The initial action in the process performed by an investigator is creating a project and collecting the submissions. Then the following steps include the preparation of data for the confirmation stage:

  • performing content analysis by selecting the existing plagiarism detection tool, where pairwise content similarity report is retrieved; and
  • acquisition of social network information and general search engine results for investigated authors.

After data are prepared, the investigator enters the confirmation stage as Figure 3.4 depicts (the names are pseudonyms). The goal of this step is to assign one of the following status to the pairwise assignments by two people who are being assessed for the possibility of plagiarism:

  • not checked - similarity between assignment has not been considered yet,
  • rejected - the assignments are not plagiarisms, and
  • confirmed — the assignments contain plagiarized sections.
Pairwise assignment view by content similarity

Figure 3.4: Pairwise assignment view by content similarity

When making the decision, the PDA tool assists the investigator by providing an extensive report of matches found on assignments submitted by different authors as depicted in Figure 3.5. There is a history of all assignments and their corresponding content similarity enriched by the social network component. The colors used depict the severity of the warnings. In this way, the tool automatically ranks detected plagiarized sections depending on information obtained from social networks and activities on the Web, but it does not confirm suspicious assignments as plagiarism. We must emphasize that the proposed approach is not intended to be used as a replacement for conventional analyses of texts, but rather as a supporting tool for increasing the efficiency of the confirmation stage. When the investigator reviews all the provided information, they can make a decision and confirm or reject plagiarism. By performing these steps, the confirmation stage of the plagiarism detection process is concluded (see Figure 3.1) and the investigation stage can begin.

Extensive report on matching, including social network information

Figure 3.5: Extensive report on matching, including social network information

One of the views in the PDA tool within the investigation stage is depicted in Figure 3.6 where the support for advanced visualization is provided. The investigator can interactively explore the semantic graph and co-occurrence matrix equipped with information about the content similarity, connectivity on social networks and general search engine results. The data from social networks and the Web are collected by means of social network public APIs and Web scraping of publicly available data about authors. As we only do a pairwise analysis of data from a limited set of people, we do not have any problems with processing resources. By visualizing the context of the entire group under investigation (eg, class at University), the investigator can carry out plagiarism detection by exploring group plagiarism.

Analytical assistance provided by the PDA tool

Figure 3.6: Analytical assistance provided by the PDA tool

4 Evaluation

4.1 Method

Our approach was evaluated on a case study of \(76\) students taking one of the lectures in Computer Science at undergraduate level. Each student had to submit five programming homework assignments during the semester that were later checked for plagiarism. There were two experiments performed with two groups of evaluators that followed different approaches to the same dataset (\(76\) students submitted 5 assignments, where \(22\) assignments were missing, so in total \(358\) assignments). Both groups of evaluators had the common goal - to identify plagiarism in the students’ work. In the first approach, \(check_{woSocio}\) evaluators employed MOSS (Schleimer, Wilkerson, and Aiken 2003) and performed a manual investigation on pairwise content similarity, while in the second approach, \(check_{wSocio}\), our method with additional social network analysis results was used. The information from social networks employed in the second approach was extracted from the public profiles of the students. In our case study, \(54\) students had publicly available information on Facebook and \(43\) students were active on Twitter. Students were informed about the use of all available public information in the process of plagiarism detection throughout the course.

The method used for the evaluation of the aforementioned approaches is a generalized linear model with logistic regression where the link function is defined as follows

\[\begin{equation} g \left ( Y \right ) = log_e \left ( \frac{n}{1-n} \right ) = \beta_0 + \sum_{j = 1}^{p} \beta_j X_j \tag{4.1} \end{equation}\]

The logistic regression is applied to a situation where the response variable \(Y = cheat_{confirmed}\) is dichotomous \((0, 1)\). The model assumes that \(Y\) follows a binomial distribution and it can be a fit to a linear model \(g(Y)\). The conditional mean of \(Y\) is the probability \(\pi = \mu_Y\) that cheat is confirmed, given a set of \(X\) values. The odds that cheat is confirmed are \(\frac{n}{1-n}\) and \(log \left ( \frac{n}{1-n} \right )\) is the log odds or \(logit\).

We have defined two models

\[\begin{equation} check_{woSocio} : cheat_{confirmed} \sim match_{cs} \tag{4.2} \end{equation}\] \[\begin{equation} check_{wSocio} : cheat_{confirmed} \sim match_{cs} + match_{fb} + match_{tw} + se_{hits} \tag{4.3} \end{equation}\]

where \(check_{woSocio}\) is a nested model within \(check_{wSocio}\) with the same response variable \(Y = cheat_{confirmed}\) and different predictors \(X_{woSocio}\) and \(X_{wSocio}\), where \(X_{woSocio} \subseteq X_{wSocio}\). The predictor variables are as follows: \(cheat_{confirmed}\) is \(\{true, false\}\) factor with information about confirmed plagiarism from investigator; \(match_{cs}\) is content similarity \(s_{ij}\) between documents \(d_i\) and \(d_j\), where \(\langle d_i, d_j, s_{ij} \rangle \in CS\); \(match_{fb}\) is a \(\{ true, false \}\) factor, based on existence of \(\left \langle p_i, p_j, \left \langle FB, follow, 1 \right \rangle \right \rangle \in SN\); \(match_{tw}\) is a \(\{ true, false \}\) factor, based on existence of \(\left \langle p_i, p_j, \left \langle TW, follow, 1 \right \rangle \right \rangle \in SN\) and \(se_{hits}\) is a number of search engine results \(n\) between people \(p_i\) and \(p_j\), where \(\left \langle p_i, p_j, KW_{ij}, n \right \rangle \in SE\) and \(KW_{ij}\) is a set of keywords including names and surnames of \(p_i\) and \(p_j\) and subject title.

The employment of models \(check_{woSocio}\) and \(check_{wSocio}\) is not intended to predict plagiarism, but rather for the ranking of potential pairwise plagiarized sections that the investigator can review and confirm in the latter stages of plagiarism detection.

To conclude our experiment, we performed a follow-through interviews with all of the students where they defended their work and evaluators determined if their submitted work was original. The evaluator’s decisions were then used for \(cheat_{confirmed}\) response variable to evaluate both models.

4.2 Results

When building a model checkwoSocio, the results show that the predictor variable matchcs is significant \(\left ( p \leq 9 \times 10^{-6} \right )\) in predicting the response variable \(cheat_{confirmed}\).

The next step was to build another model \(check_{wSocio}\) with integrated social network information, where the results of the second model show that all predictor variables \(match_{cs}\) \((p \leq 0,0025)\), \(match_{fb}\) \(\left (p \leq 0,0289 \right )\), \(match_{tw}\) \(\left (p \leq 0,0904 \right )\) and \(se_{hits}\) \(\left (p \leq 0,0432 \right )\) are significant in predicting the response variable \(cheat_{confirmed}\).

Then we were able to compare both models, which consist of variables that all have significant impact on the prediction. We performed ANOVA with likelihood ratios test (LRT) on both models. The measure used for comparison is deviance as a distance between two probabilistic models. Deviance can be regarded as a measure of lack of fit between model and data. Based on the results, we can conclude that the residual deviance in the 1st model \(check_{woSocio}\) \(\left ( RD_1 = 48,932 \right )\) is significantly higher \(\left ( p \leq 6 \times 10^{-8} \right )\) than in the 2nd model \(check_{wSocio}\) \(\left ( RD_2 = 12,479 \right )\). We can argue that the 1st model is a poorer fit to the data and that the 2nd model performs better.

To confirm that the results are meaningful, we have performed the test for overdispersion for both models that could lead to distort test standard errors and inaccurate test of significance. We performed fitting of the model twice - once with binomial family and second with quasibinomial family and the results confirmed that overdispersion is not a problem (the noncentral chi-squared test was not significant with \(p_{woSocio} = 0,977\) and \(p_{wSocio} = 0,990\)). We also assessed the model adequacy by checking for unusually high values in the hat values, studentized residuals and Cook’s D statistics. The results of these tests also confirmed that the models are adequate.

5 Discussion

As presented in the second section, the employment of advanced social network analysis approaches has been already successfully implemented in several business-oriented domains. In particular, our research was motivated from fraud detection in car insurance industry where some promising implementations can be found. The results of our approach demonstrate that there also exists a predictive value in utilizing social network information about authors of documents when detecting plagiarism in student’s work.

The main goal of SPDF is to make the overall process of plagiarism detection (FSPDP) more efficient, which has several implications for education in general by impacting all participants of FSPDP—students and teachers. With more efficient support to plagiarism detection, teachers can focus more on high-performing students, while identification of ones that employ fraudulent actions is improved. Still, the time invested by teachers also incorporates some additional activities, such as disambiguation of students on social networks, when system cannot determine the unique person from a list of available ones (eg, people with the same name). But this invested time from teacher’s perspective is rewarded with reduced set of potential pairs of documents to check for plagiarism. From the student’s point of view, the employment of their social network information in plagiarism detection can have negative acceptance. This is a general concern, as people tend to protect their own online privacy. But nonetheless, SPDF employs only publicly available information from social networking sites and general search engines. If there is no information available online about students, then the proposed approach will not improve the plagiarism detection process and we will have to utilize only content similarity and perform all work in the 3rd and the 4th stages of FSPDP manually.

During the evaluation process of SPDF, there were several cases in our study when investigators (eg, teachers) of plagiarism commended the exploratory aspect of SPDF by supporting investigation and enabling them the traversal of information about documents and authors in integrated manner of a semantic graph and a co-occurrence matrix, thus making them more efficient in evaluating the knowledge of students. Time invested to plagiarism detection varied significantly betweentwogroupsof investigatorsthatwereinvolvedinourstudy.Thefirstgroupthatemployed only content similarity information had to review \(52\) pairs of assignments identified as possible plagiarism. The other group using SPDF approach identified only \(17\) pairs of possible plagiarism. The decreased number of possible plagiarism to review and confirm was due to availability of additional social network information and advanced semantically enhanced visualization of results that enabled investigators to identify clusters of students that collaborated and therefore handling them in a group. The social information also often provided investigators some intuition on who was the author (eg, general search engine results indicated student is collaborating in an open source project related to the subject of examination) and who was copying. The enriched view on possible plagiarism also presented a stronger evidence for investigators in the last stage of investigation where students were invited to an interview and to defend their work.

Besides confirming that SPDF performs significantly better than a content similarity approach in determining plagiarism of computer programming assignments at University, we applied the approach in other settings. One of the successful studies still in progress is in a High School environment where students in a class for their mother tongue language are required to write multiple essays about literary novels. As there are several assignments per semester, instructors are overloaded with work providing feedback to all students. The employment of SPDF was therefore a welcome upgrade from manual examination and content similarity evaluation process only. Instructors, with the help of SPDF, more quickly and efficiently identify plagiarism and focus on providing constructive feedback to students to support their progress.

6 Conclusion and future work

Plagiarism detection approaches mainly focus on the first two stages of FSPDP. However, that is not sufficient to discover authors who perform unethical acts relating to plagiarism, because we also have to deal with false positive and false negative results from the analysis stage.

With the proposed approach and PDA software tool, we are able to support the investigator’s work effectively during the confirmation and investigation stages. In the confirmation stage, we can efficiently narrow the set of potential plagiarists from previous stages and in the investigation stage we can visualize the relationships among potential plagiarists with the additional semantic information. The evaluation of two different models, in the selected case study, demonstrates that the obtained results are significant. This provides evidence that the inclusion of social network information about the authors of texts assists the plagiarism detection process when compared with the approach where the information from the social networks and the Web is not employed during the manual decision-making process of confirmation and investigation of plagiarism as performed by a human investigator. In this place, it is also important to point out the restrictions of our approach. The major limitation is its dependency on publicly available data from social networks, inclination of users to provide public personal information and possibility to access these data. There also appear some ethical questions concerning automatic inquiries about user profiles but it has to be emphasized that all data from social networks and the Web that we utilized are publicly available.

Future research will focus on improving the framework by further analysis of social network connections between authors under investigation. The communication interactions will be analyzed by using advanced methods of text analysis. We will also try to find the correlation between the messages exchanged between authors and any plagiarism in their submitted documents.

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