It’s more than Facebook – Online Social Networks with Interesting Features

Posted on April 28, 2015 by Miroslaw Jaroszak

Over the last few years the online world has been dominated by few large social networks whereas there are many more – some targeted at specific communities or completely niche but with unique features. Here, we picked 8 Online Social Networks, both those commonly known and those less popular, but all with some interesting functionalities that could be incorporated into our Small.World project:

About.me – an online identities platform features:

One-page user profiles with short biographies, linked to all other social networks associated with the member.

While About.me works more like a personal website, Small.World takes an advantage of using Facebook & Google+ accounts but it’s a fully operating social network itself.

AsmallWorld – an exclusive travel and social club network, based on small worlds concept. A slight coincidence with our project’s name…

Personalised travel advice.
Members-exclusive events.
Membership on recommendation only, capped at 250,000 users.

Our service also uses travel advice but it will always be given by your trustees. And the membership is not exclusive at all.

Couchsurfing – a hospitality exchange social network.

Private hosts met travellers.
Strongly based on recommendation.
More than trust – in the past there was a credit card and address verification.

A question of trust is taken very seriously by Small.World. Our members recommend places but more importantly they recommend people.

Diaspora – a general purpose, distributed social networking service.

Decentralised (data stored on local servers).
Members can add different aspects of their lives and share certain posts/events/etc. only with people associated with certain aspect (e.g.. job-related posts/events with colleagues

Just like in Diaspora, in Small.World posts are contextual. The main difference is that the context is always geographical, i.e. related to the particular location.

Fotki – a large media sharing social networking website.

Highly customisable layout (skins and manually changed colours).
RSS feeds.

Small.World does use feeds but they are only feeds from your friends or mutual friends of yours. Small.World aims for simplicity and consistency. Users can choose from several colour themes but nothing more as cross-compatibility is the priority.

Foursquare – a search and discovery service for mobile devices.

Short tips on locations.
Search locations in surrounding area by tastes. ‘Tastes’ serve as attributes e.g. for restaurants this would display the ones that are good for karaoke, ice cream, steaks or so. Time of the day would trigger different results, e.g. restaurants serving breakfast in the morning.

Using the similar concept, both tips and “tastes” in Small.World are fuelled by your friends only. We believe that the community can provide you with the best tips, recommendations and other information you need.

Glympse – a mobile platform for tracking people to meet up with.

Real time friends tracking with detailed information.

Glympse is technically not a full-blown social network. Small.World has similar friend tracking built-in as a part of a comprehensive service. Our tracking gives you only the information you need. And it’s more secure – you can track groups of users but they will always be people you really know.

LinkedIn – popular professional, business-oriented network.

Members can see the degree to which they are connected to each other, with common characteristics such as location, skills or school.
Members can see who viewed their profile.

LinkedIn shows interrelations in the sidebar panel. Small.World goes beyond that and displays them right in your friend’s profile and points to everything you have in common.

All the web services described above have some great interesting features but it’s only Small.World that combines some of those ideas using fine-tuned solutions and provides a great integrated user experience.

Scenarios

Posted on April 25, 2015 by Jingyuan Li

A scenario provides the description of the use’s interaction with the application.
For the Small.World, let us assume the following roles:
Bob -a student who is studying the Master of Science programme in England.
He has a flatmate named Alice comes from Holland in the university.
He also has a good friend named Tess in his class.
Alice -Bob’s flatmate.
She expects to meet her parents in Amsterdam after the end of the second semester.
Tess -Bob’s classmate.
She has a nice plan about traveling to Amsterdam during the easter holiday.
System: The Small.World.

The scenarios take place in the following manner:

1.Bob enters the website of the Small.World.
2.Bob registers by his facebook account at the first time.
3.Bob allows the Small.World to search and add all his friends who use this application.
4.Bob customizes the Small.World’s layout.
5.Alice downloads the mobile app of the Small.World.
6.Alice logs in using her Google+ email account for free.
7.Alice relates her Google+ account to her facebook account.
8.Alice selects keep her personal information secret.
9.Tess receives a recommodation message from Bob about the Small.World.
10.Tess downloads the Small.World app into her phone following the link in the text message.
11.Tess logins in with her facebook account.
12.Tess puts her first review about movies by her phone.
13.The Small.World tracks Alice’s movement when she comes back to Amsterdam.
14.Tess checks-in at her new location when she travels to Amsterdam.
15.The Small.World displays Tess’s current location in a multi-layered map.
16.Bob receives a push notification-”Your friends Alice and Tess are both in Amsterdam,would you like to reccommand them to be friends?” from the Small.World.
17.Bob shares the basic information about Alice and Tess in the Small.World.
18.Bob reccommands Alice and Tess to add each other through the Small.World.
19.Alice adds Tess into her friends’ list.
20.Alice says Hello to Tess in the chat section.
21.Alice reviews Tess’s past posts.
22.Bob hosts a three-people’s video conference to discuss Tess’s travel plan in Amsterdam.
23.Tess adds more personal information in the Small.World app, such as personal hobbies and preference on food and places.
24.Alice finds a video about Anne Frank Huis and shares with Tess to recommend tess
to travel together.
25.Tess receives a sound notification from Small.World.
26.Tess accepts Alice’s recommendation.
27.Tess posts a review of locations and events.
28.Alice shares Tess’s review.
29.Alice makes an appointment with Tess in the comment area of the review to travel
together at 7th, April.
30.Bob tags Alice and Tess on a same map.
31.Tess turns on the GPS tracking.
32.The Small.World displays the current location of Tess in both online and offline mode.
33.The Small.World tracks Tess’s location in real time by GPS.
34. Tess receives an alert on presence of Alice within 5 miles proximity.
35.Alice selects a restaurant from the advertisements in Small.World app.
36.Tess gives a good feedback for the restaurant through the app.

Are you trustworthy ?

Posted on April 13, 2015 by Muna Al Abri

Trust is involved in human activities in a daily basis. Measuring trust in a digital world can be more complicated than real life. There are various methods that help in calculating trust and evaluating users based on trust value.There are significant number of models in trust in social networks. Small.World tends to follow reputation-based trust. This comprises two type of trust:

Global Trust: it is a quantitative approach where each user scored a value. The global trustworthiness then based on these scores.
Local Trust: it is a qualitative approach where the trust based on personal bias (Ziegler & Lausen, 2004).

The Local Trust are following uses three properties of trust in social networks:

Transitivity : If A trusts B and B trusts J then A will also trust J

Asymmetry : If A trusts B it does not follow that B trusts A

Personalization :Trust is held from the view of a node – it is therefore local trust (Millard & Imran, 2015).

the usage of Online Social Network (OSN) trust methodologies can boost the privacy and security for the user. A user needs to know if they can trust someone to look at there profiles or how much they can trust them. The post discuss two methods: Trust-Online Social Network (T-OSN) and Trust Indexing algorithm (TI).

The evolution of OSN encouraged people to meet strangers from all over the world and become friends. But this has also, led to some problems such as identity theft and stalking. Methods that calculate trust and help users to make decisions are usually complex or require huge resources. T-OSN is a trust model that has been created specially for OSN that can also be used on other applications such as mobile message forwarding and peer-to peer file sharing network (Li and Bont, 2011).

the model is effective, simple and require the least resources possible. the model is based on two things :

1- Number of friends (Degree)

2- Contact frequency (Contact interval)

and then a user trustworthiness can be calculated for each user. The theory suggests that if a user has more friends and more frequent communication with friends, then this user is highly secure to interact with. So the user will get a higher trustworthiness.

Another method is called “Trust Index” (Tang et al., 2012). It has the same idea as Page Rank that is being used in Google search engine. The algorithm is based on distance that counts hops. How far the user from his/her peer.

Each user will have a TI and a list of the ranking of other users, those who are more trustworthiness will be on the top of the ranking. The paper claims that TI successfully been able to classify trustworthiness users from those who are not (Tang et al., 2012).

Google have introduced Google+ circles where recommendations of people where based on common interests or people that the user might be connected to (Tang et al., 2012). Spam was not an issue. Small.World will follow the same path where trust will come in top of the recommendations that the users will get.

choosing a method to evaluate can be tricky where there is no ultimate solution. It depends on the type of the OSN and what factors will be involved in the algorithm.

research on this area is highly encouraged to create a more secure environment for OSN.

References:

Millard, D. and Imran, M. (2015). Trust: Part I.

Li, M. and Bont, A. (2011). T-OSN : a trust evaluation model in online social networks. In: IFIP Ninth International Conference on Embedded and Ubiquitous Computing. [online] Melbourne, Australia: IEEE Computer Society, pp.469-472. Available at: http://ieeexplore.ieee.org/xpl [Accessed 12 Apr. 2015].

Tang, R., Lu, L., Zhuang, Y. and Fong, S. (2012). Not Every Friend on a Social Network Can be Trusted: An Online Trust Indexing Algorithm. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. [online] Macau SAR, China: IEEE Computer Society, pp.280-285. Available at: http://ieeexplore.ieee.org [Accessed 12 Apr. 2015].

Ziegler, C. and Lausen, G. (2004). Spreading activation models for trust propagation. IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE ’04. 2004.

This post is written by : Muna & Awezan