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ABSTRACT
One fundamental issue in today On-line Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of content-based filtering. Index Terms—On-line Social Networks, Information Filtering, Short Text Classification, Policy-based Personalization.
Existing System
We believe that this is a key OSN service that has not been provided so far. Indeed, today OSNs provide very little support to prevent unwanted messages on user walls. For example, Face book allows users to state who is allowed to insert messages in their walls (i.e., friends, friends of friends, or defined groups of friends). However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them. Providing this service is not only a matter of using previously defined web content mining techniques for a different application, rather it requires to design ad-hoc classification strategies. This is because wall messages are Constituted by short text for which traditional classification Methods have serious limitations since short texts do not Provide sufficient word occurrences.
DISADVANTAGES OF EXISTING SYSTEM:
• However, no content-based preferences are supported and therefore it is not possible to prevent undesired messages, such as political or vulgar ones, no matter of the user who posts them.
• Providing this service is not only a matter of using previously defined web content mining techniques for a different application, rather it requires to design ad hoc classification strategies.
• This is because wall messages are constituted by short text for which traditional classification methods have serious limitations since short texts do not provide sufficient word occurrences.
Proposed System
learning model is concerned, we confirm in the current paper the use of neural learning which is today recognized as one of the most efficient solutions in text classification [4]. In particular, we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) for their proven capabilities in acting as soft classifiers, in managing noisy data and intrinsically vague classes. Moreover, the speed 2 in performing the learning phase creates the premise for an adequate use in OSN domains, as well as facilitates the experimental evaluation tasks.
The aim of the present work is therefore to propose and experimentally evaluate an automated system, called Filtered Wall (FW), able to filter unwanted messages from OSN user walls. We exploit Machine Learning (ML) text categorization techniques [4] to automatically assign with each short text message a set of categories based on its content. The major efforts in building a robust short text classifier are concentrated in the extraction and selection of a set of characterizing and discriminate features. The solutions investigated in this paper are an extension of those adopted in a previous work by us [5] from which we inherit the learning model and the elicitation procedure for generating pre-classified data.
The original set of features, derived from endogenous properties of short texts, is enlarged here including exogenous knowledge related to the context from which the messages originate. As far as the learning model is concerned, we confirm in the current paper the use of neural learning which is today recognized as one of the most efficient solutions in text classification [4]. In particular, we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) for their proven capabilities in acting as soft classifiers, in managing noisy data and intrinsically vague classes. Moreover, the speed 2 in performing the learning phase creates the premise for an adequate use in OSN domains, as well as facilitates the experimental evaluation tasks.
ADVANTAGES OF PROPOSED SYSTEM:
• A system to automatically filter unwanted messages from OSN user walls on the basis of both message content and the message creator relationships and characteristics.
• The current paper substantially extends for what concerns both the rule layer and the classification module.
IMPLEMENTATION
Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective.
The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.
Filtering rules
In defining the language for FRs specification, we consider three main issues that, in our opinion, should affect a message filtering decision. First of all, in OSNs like in everyday life, the same message may have different meanings and relevance based on who writes it. As a consequence, FRs should allow users to state constraints on message creators. Creators on which a FR applies can be selected on the basis of several different criteria; one of the most relevant is by imposing conditions on their profile’s attributes. In such a way it is, for instance, possible to define rules applying only to young creators or to creators with a given religious/political view. Given the social network scenario, creators may also be identified by exploiting information on their social graph. This implies to state conditions on type, depth and trust values of the relationship(s) creators should be involved in order to apply them the specified rules. All these options are formalizedby the notion of creator specification, defined as follows.
May have different meanings and relevance based on who writes it. As a consequence, FRs should allow users to state constraints on message creators. Creators on which a FR applies can be selected on the basis of several different criteria; one of the most relevant is by imposing conditions on their profile’s attributes. In such a way it is, for instance, possible to define rules applying only to young creators or to creators with a given religious/political view. Given the social network scenario, creators may also be identified by exploiting information on their social graph. This implies to state conditions on type, depth and trust values of the relationship(s) creators should be involved in order to apply them the specified rules. All these options are formalizedby the notion of creator specification, defined as follows.
Online setup assistant for FRs thresholds:
As mentioned in the previous section, we address the problem of setting thresholds to filter rules, by conceiving and implementing within FW, an Online Setup Assistant (OSA) procedure. OSA presents the user with a set of messages selected from the dataset discussed in Section VI-A. For each message, the user tells the system the decision to accept or reject the message. The collection and processing of user decisions on an adequate set of messages distributed over all the classes allows to compute customized thresholds representing the user attitude in accepting or rejecting certain contents. Such messages are selected according to the following process. A certain amount of non-neutralmessages taken from a fraction of the dataset and not belonging to the training/test sets, are classified by the ML in order to have, for each message, the second level class membership values.
Blacklists:
A further component of our system is a BL mechanism to avoid messages from undesired creators, independent from their contents. BLs are directly managed by the system, which should be able to determine who are the users to be inserted in the BL and decide when users retention in the BL is finished. To enhance flexibility, such information
Are given to the system through a set of rules, hereafter called BL rules. Such rules are not defined by the SNM, therefore they are not meant as general high level directives to be applied to the whole community. Rather, we decide to let the users themselves, i.e., the wall’s owners to specify BL rules regulating who has to be banned from their walls and for how long. Therefore, a user might be banned from a wall, by, at the same time, being able to post in other walls.
Similar to FRs, our BL rules make the wall owner able to identify users to be blocked according to their profiles as well as their relationships in the OSN. Therefore, by means of a BL rule, wall owners are for example able to ban from their walls users they do not directly know (i.e., with which they have only indirect relationships), or users that are friend of a given person as they may have a bad opinion of this person. This banning can be adopted for an undetermined time period or for a specific time window. Moreover, banning criteria may also take into account users’ behavior in the OSN. More precisely, among possible information denoting users’ bad behavior we have focused on two main measures. The first is related to the principle that if within a given time interval a user has been inserted into a BL for several times, say greater than a given threshold, he/she might deserve to stay in the BL for another while, as his/her behavior is not improved. This principle works for those users that have been already inserted in the considered BL at least one time. In contrast, to catch new bad behaviors, we use the Relative Frequency (RF) that let the system be able to detect those users whose messages continue to fail the FRs. The two measures can be computed either locally, that is, by considering only the messages and/or the BL of the user specifying the BL rule or globally, that is, by considering all OSN users walls and/or BLs.
FILTERED WALL ARCHITECTURE
Three Tier architecture is used in OSN services. Thesethree layers are
• Social Network Manager (SNM)
• Social Network Application (SNA)
• Graphical User Interface (GUI)
1.Social Network Manager (SNM)
The initial layer is Social Network Manager layerprovides the essential OSN functionalities (i.e., profileand relationship administration).It also maintains all the
data regarding to the user profile.[2] After maintainingand administrating all users data will provide for secondlayer for applying Filtering Rules (FR) and Black lists
(BL).
2. Social Network Application (SNA)
In second layer Content Based Message Filtering(CMBF) and Short Text Classifier is composed. This isvery important layer for the message categorizationaccording to its CBMF filters. Also Black list ismaintained for the user who sends frequently bad wordsin message.
3. Graphical User Interface (GUI)
Third layer provides Graphical User Interface to the user who wants to post his messages as a input.In this layer Filtering Rules (FR) are used to filter theunwanted messages and provide Black list (BL) for theuser who are temporally prevented to publish messageson user’s wall.
IV. MATHEMATICAL MODEL
A. For Filtering Rules:
1) Input
Filtering Rules are customizable by the user. User canhave authority to decide what contents should be blockedor displayed on his wall by using Filtering rules. Forspecify a Filtering rules user profile as well as user socialrelationship will be considered.
FR= {Actor, UserSpec, ContentSpec}
FR is dependent on following factors
• Author
• UserSpec
• ContentSpec
• Action
Author is a person who defines the rules.
UserSpecdenotes the set of OSN user.
ContentSpecis a Boolean expression defined on
content.
2) Process
FM={UserSpec,contentSpec==category(Violence,Vulgar,offensive,Hate,Sextual)}
• FM
• UserSpec
• ContentSpec
Here,
FM Block Messages in percentage
UserSpecDenotes set of users
ContentSpecCategory of specified contents in message.
In processing, after giving input message, the systemwill compare the text with the different categories whichare prevented. If messege found in that prevented type of
category then message will display to the user that “can’tsend this type of messages.”
Process denotes the action to be performed by thesystem on the messagesmatching Content- Spec andcreated by users identified by UserSpec.
3) Output
PFM= {ContentSpec, M|Y}
• PFM Percentages of filtered message in a year
or month.In general, more than a filtering rule can apply to thesame user. A message is therefore published only if it isnot blocked by any of the filtering rules that apply to themessage creator.
B. Blacklists
BLs are directly managed by the system,This should be able to determine who are the users tobe inserted in the BL and decide when users’ retention inthe BL is finished. To enhance flexibility, suchinformation is given to the system through a set of rules,hereafter called BL rules.
Definition 3 (BL rule).
1) INPUT
INPUT = {Actor, UserSpec, UserBehavior} Where
• author is the OSN user who specifies the rule,
i.e., the wall owner;
• UserSpec is a creator specification, specified
according to Definition 1;
• UserBehavior consists of Message sending
category of User.
2) Process
BL={UserSpec,ContentSpec,T}
• UserSpec
• ContentSpec
• T
UserSpecCreator Specification
ContentSpecMessage send by User.
T Messages is the total number of messages that
each OSN user sent.
3) Output
BL={UserSpec,ContentSpec,T>3 ,P}
• UserSpec
• ContentSpec
• T > 3
UserSpecCreator Specification
ContentSpecMessage send by User.
T Prevented Message count is greater than 3 timesthen Messgecreator will put into Black listautomatically for specific time period P
FUTURE SCOPE
• Future scope of this system is that Image Filtering Techniques.
• In our system we can only filter the text messages. So Image filtering will be tried in our future system.
CONCLUSION
Existing system is used to filter undesired messagesfrom OSNs wall using customizable filtering rules (FR)enhancing through Black lists (BLs).In present system (www.winow.in), we are more focuson an investigation of two interdependent tasks in depth. This system approach decides when user should beinserted into a black list. The system developed GUI and a set of tools whichmake BLs and FRs specifications more simple and easy. Investigation tools may be able to automaticallyrecommend trust value of the user. The primary work ofthis system is to find out trust values used for OSNaccess control. In this system we will provide only coreset of functionalities which are available in current OSNslike Facebook, Orkut, Twitter,etc. In existing OSNs havesome difficulties in understanding to the average usersregarding privacy settings. But this problem will beovercome in present OSNs system.