Does showing off help to make friends? Experimenting a sociological game on self-exhibition and social networks
Used the site http://socialgeek.com to gather data on how people would choose to portray themselves on a social network site. Showed them various types of pictures (provocative, standard, showing off, body immodesty) and asked whether they would use the photo as a profile picture.
Found a correlation between number of friends and the self-exhibition. They suggest that people may use ‘show off’ type pictures to gain online friends.
Also found that people like to be friends with people like them (similar age, socio-economic session, etc). Except people in the study preferred to be friends with women.
I think this is sort of related to our study of online profile photos, but I’m not exactly sure how. This paper used a different personality scale, so I’m not clear how we can compare the two.
What Are They Blogging About? Personality, Topic and Motivation in logs
One way to categorize motivation to blog:
- Internal (documetning lfe, catharsis)
- External (Interests, Opinions)
Using the Five Factor Personality Model to make some hypotheses about personality. Did some text analysis on a blog corpus from BlogMetrics using LIWC text analysis tool, as implemented in TAWC. For bloggers high in these factors:
Neuroticism: self-therapy/catharsis – focusing on self and venting purely negative feelings.
Extraversion: Talk alot about themselves and other people. Use lots of 1st person, 2nd person, 3rd person pronouns. Used lots of positive emotion words.
Openness: Review/evaluation of leasure interests from personal perspective
Conscientiousness: Faithfully document life going on around them, references to others. Lots of talk about their job, people around them.
Agreeableness: positive self-talk focus, negative emotions and leisure activities avoided.
As part of the tutorials yesterday, I took a simple Five Factor Personality test, where I scored high on Agreeableness and very low on Neuroticism. I’d like to look at my blogs and see these findings describe my own behavior.
A Social Identity Approach to identify Familiar Strangers in a Social Network
Familiar Strangers: People you observe repeatedly, but do not know each other. In real life, people you see daily on the train. Online, similar blogging behavior, interests, but not on the same social network. It would be nice to find these people, to understand more niche interests, do predictive modeling and trend analysis, etc.
This is interesting because it focuses on trying to find and connect people with narrow, niche interests (the long tail of the blogosphere).
They use a Social Identify approach. People cluster contacts into meaningful groups. So we really propagate the search through relevant clusters of contacts. We limit the search space.
Used blog tags and content to generate a vector that describes a blog, and then calculated similarity using cosine adjacency. Clustered with k means. Compared their results against 1) exhaustive approach, 2) random approach.
Results indicate the Social Identify approach has accuracy between 80-90%, depending on the dataset — much better accuracy than random, but much faster than an exhaustive search.
This research assumes an egocentric search. You look first at people that are connected to you in the network. But that doesn’t seem realistic. I can find familiar strangers on sites like delicious.com or twitter via tag cloud, rather than searching first through my contacts. I asked the speaker this question. He suggested that his approach would be helpful in locating people near the cluster of people that use a particular tag, but not the precise tag.
You Are Where You Edit: Location Wikipedia Contributors through Edit Histories
Exploring the increasingly prevalent role of geography on the web. Allows geographically informed content retrieval, filtering. Potential invasion of privacy. Looked at Wikipedia Geopages – pages that correspond to a physical location in the real world, with lat/long coordinates.
This paper wants to know if we can characterize the location of the people who contribute to geopages. Used DBPedia to bootstrap finding the geopages. There’s a tradeoff in that wikipedia only collects single point, instead of an extent/area.
330K geopages. They want to find contributors with a large number of edits to geopages constrained to a small area (~ 70mi x 70mi).
Over half of contributors make most of their edits on 1-2 pages. Looked at 100 random user pages to determine motivation: most people live in that place, or where born there.