Social Media and Public Awareness Author: Dushyant Tanna1; Zenit Raval2; Dhwani Raval3 Affiliation: Assistant Professor, Dept. of Mathematics, Marwadi Engineering College, India1; Marketing and Sales Engineer, Rajoo Engineering, India2; VIT university, India3 E-mail:
[email protected];
[email protected];
[email protected] ABSTRACT The fundamental idea of a social network is very simple though effective. A social network is a set of actors (or points, or nodes, or agents) that may have relationships (or edges, or ties) with one another. Networks may have any number of actor, and one or more kinds of relations among actors. In order to have an applicable understanding of a social network, a through and rigorous description of a pattern of social relationships is a necessary initial point for analysis. That is, ideally we will know about all of the ties among each pair of actors in the population. In order to describe any type of network connection, we need some amount of information to manage these data and to manipulate them so that we can see patterns of social structure which are sometimes tedious and complicated. All of the tasks of social network methods are made easier by using tools from mathematics. Information is recorded as in the form of matrices which are essential tool for the manipulation of network data and the calculation of indexes describing networks. Other useful mathematical tool to visualize the patterns of relations and connection, graphs are often useful.
Keywords: Social Network analysis, Reachability, Walk, Node, Ego, Geodesic Distance
1. INTRODUCTION
Over the last decade Social networking sites have become very famous spot of attraction for people to communicate with family, friends and colleagues from around the corner or across the globe. On one hand there are lots of benefits from the collaborative, distributed approaches promoted by sincere use of social networking sites, there are information security and privacy concerns. It has become lot easier for malicious people to avail for the good volume of personal information on social networking sites and they can easily exploit this information. On one side the technology we demand to make use of SN sites easier to exchange the data which at the same time malware that can shut down an organization's networks, or keystroke loggers that can steal credentials. Common social networking risks such as spear phishing, social engineering, spoofing, and web application attacks attempt to steal a person's identity. Such attacks are often successful due to the
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assumption of being in a trusting environment social networks create. There is always a great amount of concern for the Security and privacy related to social networking sites. These are fundamentally behavioural issues not technology issues. Innocent teenager and youth keep on positing more and more information without being aware of the malicious intentions. It has become very routine that People often provide private, sensitive or confidential information about themselves and other people, wittingly or unwittingly but this pose a higher risk to themselves and others. Certain Information like a person's social security number, address, phone or mobile number, financial information or confidential business information should never be published online. In the same fashion posting photos, videos or audio files could lead to an organization's breach of confidentiality or an individual's breach of privacy. So one must be aware of the risk before posting such photos or be ready for them to be used by malicious persons in future.
2. DEFINITIONS 2.1. Node
It represents the individual actor in the network.
2.2. Ego
It is an individual focal node. In a network the number of ego is the same as the number of nodes. It can be groups, persons or entire society.
2.3. Walk
The most general form of connection between two actors in a graph is called a walk. A walk is a sequence of actors and relations that begins and ends with actors.
2.4. Reachability
An actor is "reachable" by another if there exists any set of connections by which we can trace from the source to the target actor, regardless of how many others fall between them. If the data are asymmetric or directed, it is possible that actor A can reach actor B, but that actor B cannot reach actor A
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2.5. Geodesic Distance
The geodesic distance (for directed and undirected data) is the number of relations in the shortest possible walk from one actor to another (or from an actor to themselves).
3. SCALE OF MEASUREMENTS 3.1 Binary Measure of Relations:
This is one of the most common approach to scaling (assigning numbers to) relations is to simply distinguish between relations being absent (coded zero), and ties being present (coded one). If we ask respondents in a survey to tell us "which other people on this list do you like?" we are doing binary measurement. Each person from the list that is selected is coded one. Those who are not selected are coded zero. Much of the development of graph theory in mathematics, and many of the algorithms for measuring properties of actors and networks have been developed for binary data. Binary data is so widely used in network analysis that it is not unusual to see data that are measured at a "higher" level transformed into binary scores before analysis proceeds. To do this, one simply selects some "cut point" and re-scores cases as below the cut-point (zero) or above it (one). Dichotomizing data in this way is throwing away information. The analyst needs to consider what is relevant (i.e. what is the theory about? is it about the presence and pattern of ties, or about the strengths of ties?), and what algorithms are to be applied in deciding whether it is reasonable to recode the data. Very often, the additional power and simplicity of analysis of binary data is "worth" the cost in information lost.
3.2 Multiple-Category nominal measures of relation
In collecting data we might ask our respondents to look at a list of other people and tell us: "for each person on this list, select the category that describes your relationship with them the best: friend, lover, business relationship, kin, or no relationship." We might score each person on the list as having a relationship of type "1" type "2" etc. This kind of a scale is nominal or qualitative -- each person's relationship to the subject is coded by its type, rather than its strength. Unlike the binary nominal (truefalse) data, the multiple category nominal measure is multiple choices. The most common approach to analysing multiple-category nominal measures is to use it to create a series of binary measures. That is, we might take the data arising from the question described above and create separate sets of scores for friendship ties, for lover ties, for kin ties, etc. This is very similar to "dummy coding" as a way of handling multiple choice types of measures in statistical analysis. In examining the resulting data, however, one must remember that each node was allowed to have a tie in at most one of the resulting networks. That is, a person can be a friendship tie or a lover tie - but not both -- as a result of the way we asked the question. In examining the resulting networks,
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densities may be artificially low, and there will be an inherent negative correlation among the matrices.
3.3 Grouped Ordinal measures of relations
One of the earliest traditions in the study of social networks asked respondents to rate each of a set of others as "liked" "disliked" or "neutral." The result is a grouped ordinal scale (i.e., there can be more than one "liked" person, and the categories reflect an underlying rank order of intensity). Usually, this kind of three point scale was coded -1, 0, and +1 to reflect negative liking, indifference, and positive liking. When scored this way, the pluses and minuses make it fairly easy to write algorithms that will count and describe various network properties (e.g. the structural balance of the graph). Grouped ordinal measures can be used to reflect a number of different quantitative aspects of relations. Network analysts are often concerned with describing the "strength" of ties. But, "strength" may mean (some or all of) a variety of things. One dimension is the frequency of interaction - do actors have contact daily, weekly, monthly, etc. Another dimension is "intensity," which usually reflects the degree of emotional arousal associated with the relationship (e.g. kin ties may be infrequent, but carry a high "emotional charge" because of the highly ritualized and institutionalized expectations). Ties may be said to be stronger if they involve many different contexts or types of ties. Summing nominal data about the presence or absence of multiple types of ties gives rise to an ordinal (actually, interval) scale of one dimension of tie strength. Ties are also said to be stronger to the extent that they are reciprocated. Normally we would assess reciprocity by asking each actor in a dyad to report their feelings about the other. However, one might also ask each actor for their perceptions of the degree of reciprocity in a relation: Would you say that neither of you like each other very much, that you like X more than X likes you, that X likes you more than you like X, or that you both like each other about equally?
3.4 Full-rank ordinal measures of relations
Sometimes it is possible to score the strength of all of the relations of an actor in a rank order from strongest to weakest. For example, I could ask each respondent to write a "1" next to the name of the person in the class that you like the most, a "2" next to the name of the person you like next most, etc. The kind of scale that would result from this would be a "full rank order scale." Such scales reflect differences in degree of intensity, but not necessarily equal differences -- that is, the difference between my first and second choices is not necessarily the same as the difference between my second and third choices. Each relation, however, has a unique score (1st, 2nd, 3rd, etc.). Full rank ordinal measures are somewhat uncommon in the social networks research literature, as they are in most other traditions. Consequently, there are relatively few methods, definitions, and algorithms that take specific and full advantage of the information in such scales. Most commonly, full rank
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ordinal measures are treated as if they were interval. There is probably somewhat less risk in treating fully rank ordered measures (compared to grouped ordinal measures) as though they were interval, though the assumption is still a risky one. Of course, it is also possible to group the rank order scores into groups (i.e. produce a grouped ordinal scale) or dichotomize the data (e.g. the top three choices might be treated as ties, the remainder as non-ties). In combining information on multiple types of ties, it is frequently necessary to simplify full rank order scales. But, if we have a number of full rank order scales that we may wish to combine to form a scale (i.e. rankings of people's likings of other in the group, frequency of interaction, etc.), the sum of such scales into an index is plausibly treated as a truly interval measure.
3.5 Interval Measure of relations
The most "advanced" level of measurement allows us to discriminate among the relations reported in ways that allow us to validly state that, for example, "this tie is twice as strong as that tie." Ties are rated on scales in which the difference between a "1" and a "2" reflects the same amount of real difference as that between "23" and "24." True interval level measures of the strength of many kinds of relationships are fairly easy to construct, with a little imagination and persistence. Asking respondents to report the details of the frequency or intensity of ties by survey or interview methods, however, can be rather unreliable -- particularly if the relationships being tracked are not highly salient and infrequent. Rather than asking whether two people communicate, one could count the number of email, phone, and inter-office mail deliveries between them. Rather than asking whether two nations trade with one another, look at statistics on balances of payments. In many cases, it is possible to construct interval level measures of relationship strength by using artefacts (e.g. statistics collected for other purposes) or observation.
4. SOME HARD CORE FACTS PRESENTED WITH SIMPLE PERCENTAGE TABLE 4.1 Do you know how to use privacy setting on your SN sites? The following table gives us the evidence about the usage of the privacy setting on SN sites. People were asked to categorise the awareness about how to use to privacy setting on SN sites and their responses were recorded as in the below given table. Privacy Setting Frequency Percentage I had read it once 13 26 I know a bit of it 7 14 No 8 16 One of my friend once 3 6 told me about it Yes I have read it 19 38 thoroughly Total 50 100 Table 1: Use of Privacy Setting
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Figure 1: Use of Privacy Setting So it is evident that only 38% have gone through the privacy setting usage of SN sites.
4.2 Frequency of changes in Privacy Setting Frequency of changes
Frequency
Percentage
Every month
5
10
Every Week
4
8
Everyday
1
2
Never
17
34
Once
23
46
50
100
Table 2: Frequency of changes in settings
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Figure 3: Acceptance of request
Figure 2: Frequency of Changes This is obvious that 80% changed once or never.
It is obvious that only 40% never accepts this. Rest 60% will accept sometime or often.
4.3 Acceptance of request based on gender
4.4 Acceptance of request based on profile
Acceptance
Frequency
Percentage
Acceptance based on Profile
Frequency
Percentage
Always
6
12
Always
15
30
Never
20
40
Never
12
24
Rarely
8
16
Rarely
10
20
Sometimes
8
16 Sometimes
8
16
Very Rare
5
10
Total
50
100
very rare
8
50
16
100
Table 3: Acceptance of request Table 4: acceptance based on profile
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Figure 5: Stoppage of usage of SN sites Figure 4: Acceptance of request based on profile.
It is evident that 92% of the people will prefer to stop using the SN site for any misuse.
From the chart it is evident that 70% people accept the request based on good profile.
REFERENCES
4.5 Prohibition of use for unexpected usage by SN sites or friends Stoppage from using the SN sites if found the unexpected use of information by SN sites authorities or friends
[1] Borgatti, S.P. & Foster, P.C. 2003. The network paradigm in organizational research: A review and typology. Journal of Management, 29: 9911013
Frequency
Percentage
Always
27
54
Never
4
8
Rarely
5
10
Sometimes
12
24
Very Rare
2
4
[2] Belson, William (1981). The design and understanding of research questions. Hants, England: Garner Publishing. [3] Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside [4] Bradburn, N., Sudman, S., & Wansink, B. (2004). Asking questions: the definitive guide to questionnaire design. San Francisco: Jossey-Bass. [5] Waller J.L., Johnson M. H., (2013), Chi-Square and T-Tests Using SAS®: Performance and Interpretation, Georgia Regents University, Augusta, Georgia, SAS Global Forum 2013, Paper 430-2013 [6] Moore, D. S., 2010. The Basic Practice of Statistics. Fifth edition. W. H. Freeman and Company, New York, NY, USA [7] RemenyiD., Onofrei G., English J., (2009), An Introduction to Statistics using Microsoft Excel, Academic Publishing Limited, UK
Table 5: Stopping the usage
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[8] R.A. Fisher and F. Yates, Statistical Tables for Biological Agricultural and Medical Research, 6th ed., Table IV, Oliver & Boyd, Ltd., Edinburgh
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[9] Tanna Dushyant, Social Network Analysis – Survey on privacy of personal data, IJSHRE, Volume 1, Issue 4, 2013 [10] Tanna Dushyant, Harmonious labelling of certain graphs, IJAERS, Volume 2, Issue 4, 2013 [11] Tanna Dushyant, Labeling of Double Triangular Snake, IJAERS, Volume 2, Issue 4, 2013.
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