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The rise in popularity of Twitter has led to a debate on its impact on public opinions. The optimists foresee an increase in online participation and democratization due to social media's personal and interactive nature. Cyber-pessimists, on the other hand, explain how social media can lead to selective exposure and can be used as a disguise for those in power to disseminate biased information. To investigate this debate empirically, we evaluate Twitter as a public sphere using four metrics: equality, diversity, reciprocity and quality. Using these measurements, we analyze the communication patterns between individuals of different hierarchical levels and ideologies. We do this within the context of three diverse conflicts: Israel-Palestine, US Democrats-Republicans, and FC Barcelona-Real Madrid. In all cases, we collect data around a central pair of Twitter accounts representing the two main parties. Our results show in a quantitative manner that Twitter is not an ideal public sphere for democratic conversations and that hierarchical effects are part of the reason why it is not.Keywords: public sphere, social stratification, conflict, political communication, twitter IntroductionWith the rapid growth of Twitter, it has become one of the most widely adopted platforms for online communication. Besides using it for relationship formation and maintenance, many people also regularly engage in discussions about controversial issues [1]. On one hand, this increasing adoption of Twitter for online deliberation inevitably creates a perfect environment for open and unrestricted conversations. On the other hand, individuals on Twitter tend to associate more with like-minded others and to receive information selectively. This leads the cyber-pessimist to emphasize the vital role of opinion leaders in shaping others' perceptions during a conflict and to foresee the online environment as a disguise for those in higher social hierarchy to disseminate information. In order to empirically understand whether Twitter creates a public sphere for democratic debates we ask questions like: How do people on different sides of ideological trenches engage with each other on Twitter? How much does social stratification matter in this process? And how universal are such patterns across different types of polarized conflicts?For our study, we choose three conflicts of very different nature: the Palestine-Israel conflict, the Democrat-Republication political polarization, and the FC Barcelona-Real Madrid football rivalry. Our analysis is guided by four assessment metrics for the democratic public sphere introduced by [2], namely, (i) equality, (ii) diversity, (iii) reciprocity, and (iv) quality. We find that in general Twitter is not an idealized space for democratic, rational cross-ideological debate, as individuals from the bottom social hierarchy not only interact less but also provide lower quality comments in inter-ideological communication. We believe our results advance the understanding of opportunities and limitations provi...

With the increasing growth and popularity of social networking sites, social question and answering has become a venue for individuals to seek and share information. This study evaluates eleven extrinsic factors that may influence the response rate in social question and answering. These factors include the number of followers, the frequency of posting, the number of at-mentioned recipients, whether or not a question contains any at-mentioned verified account, unverified account, hashtag, emoticon, expression of gratitude, repeated punctuation and interjections, as well as the topic and the posting time period of a question. We collected and analyzed over 10,000 questions from Sina Weibo. Eight out of all eleven features were found to significantly predict the number of responses received. We believe that our study is of significant value in providing insights for the design and development of future social question and answering tools, as well as enhancing the collaboration among social network users in supporting social information seeking activities.

The goal of this research is to evaluate the effect of ad rank on the performance of keyword advertising campaigns. We examined a large-scale data file comprised of nearly 7,000,000 records spanning 33 consecutive months of a major US retailer's search engine marketing campaign. The theoretical foundation is serial position effect to explain searcher behavior when interacting with ranked ad listings. We control for temporal effects and use one-way analysis of variance (ANOVA) with Tamhane's T2 tests to examine the effect of ad rank on critical keyword advertising metrics, including clicks, cost-per-click, sales revenue, orders, items sold, and advertising return on investment. Our findings show significant ad rank effect on most of those metrics, although less effect on conversion rates. A primacy effect was found on both clicks and sales, indicating a general compelling performance of top-ranked ads listed on the first results page. Conversion rates, on the other hand, follow a relatively stable distribution except for the top 2 ads, which had significantly higher conversion rates. However, examining conversion potential (the effect of both clicks and conversion rate), we show that ad rank has a significant effect on the performance of keyword advertising campaigns. Conversion potential is a more accurate measure of the impact of an ad's position. In fact, the first ad position generates about 80% of the total profits, after controlling for advertising costs. In addition to providing theoretical grounding, the research results reported in this paper are beneficial to companies using search engine marketing as they strive to design more effective advertising campaigns.

With the advance of modern technology, social networking sites, such as Twitter, are becoming increasingly important information sources for people to find answers to their questions. Given such trend, in this project, we report results from our analysis of 10,000 English-written question tweets with expectations of helpful answers (which we call "information seeking tweets" in the following paper) collected in one week period. We explore the topical characteristics and patterns demonstrated in people's information seeking behaviors under online social contexts. In particular, through our topical comparisons between social search, traditional search and real time search, we find that social information seekers show more personalized requirements and more timely needs. Technology, healthcare and education related questions appeared extremely frequent among questions asked on Twitter, along with a desire to pursue help from experts in these areas. In addition to our findings on the topical domains, we also observe that social search contains a significantly less proportion of direct communication than general tweets, showing user's relatively higher openness to diversified answers. Our results also indicate the important role that time and location play in social information seeking context. Based on these findings, implications for future design of social search systems or tools are discussed at the end.