Thank U all!!

Thanks for your attention & thanks all reference material !

Reference List

Choi, S. (2014). The two-step flow of communication in twitter-based public forums. Social Science Computer Review, 33(6), 696-711.

Key Fact, Violence against women. World Health Organization Official Website. Retrieved on 27th April, 2018 from:http://www.who.int/news-room/fact-sheets/detail/violence-against-women

Kietzmann, J. H., Hermkens, K., Mccarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.

Loney-Howes, R. (2017). #MeToo, Rape Culture and the Paradoxes of Social Media Campaigns. Retrieved on 27th April, 2018 from:https://www.researchgate.net/publication/322714535_MeToo_Rape_Culture_and_the_Paradoxes_of_Social_Media_Campaigns

#TAGS: https://tags.hawksey.info/

Voyant visualization:http://www.voyant-tools.org/?corpus=e51012a06cede96a43a5608d62071d7f

Whiting, A., & Williams, D. (2013). Why people use social media: a uses and gratifications approach. Qualitative Market Research, 16(4), 362-369(8).

THANKU.jpg

Advertisements

Concluding discussions

Some limitations:
1. The first one is data validity. Because of large amount of data are existing, how to choose useful and meaningful data becomes more important and difficult. Some accounts looks like personal account, actually they are operated by third party or organization or company who has specific aim.
2. Second limitation is data authenticity. It is the common issue for all researchers no matter online or offline researchers. Researchers cannot judge the information on whether followers are real or false.
3. Third limitation is how to make the research result deeply. Big data gives us a huge amount of information, but everything happens must relate to other reasons or causes. When analyzing a situation or an event, you should across more other data.
4. The next one is predictability. Even we get the big data very validity, authenticity, and deeply, it is just the result of now or before. We can use them for references, but not for judging the future trade.
5. TAGS v6.1.8. only provides 7 days data before the collect day. If we want to analyses a phenomenon or a long term movement, it hard to get useful data.
6. For the content analyses, knowledge gap, and cultural difference makes coding more difficult, because sometimes twitters use abbreviation or slang word.

Key conclusions:
Social media had already became a part of people’s lives. It brings us a number of data and speed up information transmission. The information transparency is now a growing threat to politicians and authorities. We can spread information from lower to upper class. No matter what you focus on, you always can find like-minded people in the other end of the network. As a social issue, more and more people put a premium on sexual assaults. Everyone wants to be equal treatment by others.

Broader implications

Social networks make the distance between people shorter than ever. Media users can get dozens of information every day. Audiences should increase their resolving ability to be an information filter and gate keeper by themselves.

Furthermore, people should flexible use of new technology and new skills to adopt the galloping development of society. When using social networks, people should be conscious of strong ethics with impeccable integrity, cannot spread falsehoods and rumors.

Overall, sexual assault is a social problem, people could analyze deeper in policy or cultural or psychological for both perpetrators and victims.

A data visualization via Gephi

Gephi Zoom IN.JPG

Visualization showing exhibits top 6 significant clusters in different colors. Clusters are generated by analyzing the retweets with hashtag #sexualassault on Twitter by using Gephi. Those names are the accounts with weighted degree more than 15.

The biggest pink ball called WakingLifeDream, which is more active in political or social topics. Moreover, this cluster has a strong relationship with madasdnam and Kaceyhells.

Three isolated clusters are PPFA (an organization of health care), BeyondClarityX (Mental Health radio), and Joy105com (news website). They have their own target audiences to get involved.

Two data visualizations via Voyant

final voyant1.JPG

This visualization is called Cirrus. It is a basic word cloud displaying the frequently used words. I make the terms of 25, which are top 25 words occurring more frequently. The size of words refer to the frequency of its occurrence. Larger means more frequency, the rest can be done in the same manner. For instance, hashtags #metoo movement, target audiences containing women, girl, or teenage, and media practitioner such as Pence, frequently appear in this topic.

 

final bubblelines.png

Bubblelines visualizes the frequency and repetition of a word’s use in a collected works. Each document in the collections is represented a horizontal line and divided into equally length. It also indicates the word’s frequency in the corresponding of text. The larger size of bubble’s radius presents the more frequently the word occurs. In this graph, green line represents the teenage, pinkish purple is our topic “sexual assaults,” the blue means #metoo movement, the forth one represents children, and the last one refers to women.

Two visualizations via Tableau

Locations.JPG

The first visualization exhibits the location that sexual assault is a global issue. Besides, the United State as a country with most Twitter users has the biggest population taking participant in sexual assault.

User Activity and Visibility.jpg

The second graph illustrates the comparison between the User Activity and User Visibility. In the User Activity part, people mention AshleyPekin89 the most frequently that means he or she is the most activity account when people talk about sexual assault. In the user visibility area, most tweets belong to retweet type, which means twitter account like madasednam may be the opinion leader in this area. They may not the most activity user, but they are the most influential user in this topic.

Methodology – Data collection (TAGS)

大数据.jpg

“Sexualassault” as a hashtag.

The script ran daily from April 13 to 20, 2018 by TAGS v6.1.8. (TAGS is a free Google Sheet template which lets you setup and run automated collection of search results from Twitter).

The advantages of Twitter:

  • More transparent. The information of users or tweets is almost open and capturable.
  • Simultaneously, the data of Twitter is very specific. People can choose a specific period and a type of tweets including original, retweet or @mentioned.

The raw dataset has 2806 unique tweets. I selected 150 tweets for content analyses(I did not delete non-English tweets, because sexual assault is a global issue, I can use translate software to help me to understand tweets content).

 

Horrifying violence against women key facts by WHO!!!

Nearly 35% of women have experienced either physical and/or sexual intimate partner violence or non-partner sexual violence in their lifetime in the world;

Around 38% of murders of women are committed by a male intimate partner!!!

What a horrible thing for US!!!

Always SAY NO!!!!!

reference:

http://www.who.int/news-room/fact-sheets/detail/violence-against-women